{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import utils   # some convenient functions\n",
    "import datetime\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Johns Hopkins has international data.  And US data down to county level after 3/22/2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "jhs_data = utils.load_jhs_raw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Update_Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-11</th>\n",
       "      <td>3046.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-12</th>\n",
       "      <td>3056.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-13</th>\n",
       "      <td>3062.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-14</th>\n",
       "      <td>3075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-15</th>\n",
       "      <td>3085.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Deaths\n",
       "Update_Date        \n",
       "2020-03-11   3046.0\n",
       "2020-03-12   3056.0\n",
       "2020-03-13   3062.0\n",
       "2020-03-14   3075.0\n",
       "2020-03-15   3085.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china = jhs_data[(jhs_data['Country_Region'] == 'China') & (jhs_data['Province_State'] == 'Hubei')]\n",
    "pd.pivot_table(china, index='Update_Date', values='Deaths', aggfunc='sum').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Last_Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long_</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Admin2</th>\n",
       "      <th>Active</th>\n",
       "      <th>Combined_Key</th>\n",
       "      <th>Update_Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-03-11 10:53:02</td>\n",
       "      <td>67773.0</td>\n",
       "      <td>3046.0</td>\n",
       "      <td>49134.0</td>\n",
       "      <td>30.9756</td>\n",
       "      <td>112.2707</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>15593.0</td>\n",
       "      <td>Hubei,China</td>\n",
       "      <td>2020-03-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-03-12 09:53:06</td>\n",
       "      <td>67781.0</td>\n",
       "      <td>3056.0</td>\n",
       "      <td>50318.0</td>\n",
       "      <td>30.9756</td>\n",
       "      <td>112.2707</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>14407.0</td>\n",
       "      <td>Hubei,China</td>\n",
       "      <td>2020-03-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-03-13 11:09:03</td>\n",
       "      <td>67786.0</td>\n",
       "      <td>3062.0</td>\n",
       "      <td>51553.0</td>\n",
       "      <td>30.9756</td>\n",
       "      <td>112.2707</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>13171.0</td>\n",
       "      <td>Hubei,China</td>\n",
       "      <td>2020-03-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-03-14 10:13:09</td>\n",
       "      <td>67790.0</td>\n",
       "      <td>3075.0</td>\n",
       "      <td>52960.0</td>\n",
       "      <td>30.9756</td>\n",
       "      <td>112.2707</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>11755.0</td>\n",
       "      <td>Hubei,China</td>\n",
       "      <td>2020-03-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-03-15 18:20:18</td>\n",
       "      <td>67794.0</td>\n",
       "      <td>3085.0</td>\n",
       "      <td>54288.0</td>\n",
       "      <td>30.9756</td>\n",
       "      <td>112.2707</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>10421.0</td>\n",
       "      <td>Hubei,China</td>\n",
       "      <td>2020-03-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province_State Country_Region         Last_Update  Confirmed  Deaths  \\\n",
       "0          Hubei          China 2020-03-11 10:53:02    67773.0  3046.0   \n",
       "0          Hubei          China 2020-03-12 09:53:06    67781.0  3056.0   \n",
       "0          Hubei          China 2020-03-13 11:09:03    67786.0  3062.0   \n",
       "0          Hubei          China 2020-03-14 10:13:09    67790.0  3075.0   \n",
       "0          Hubei          China 2020-03-15 18:20:18    67794.0  3085.0   \n",
       "\n",
       "   Recovered      Lat     Long_  FIPS Admin2   Active Combined_Key Update_Date  \n",
       "0    49134.0  30.9756  112.2707   NaN         15593.0  Hubei,China  2020-03-11  \n",
       "0    50318.0  30.9756  112.2707   NaN         14407.0  Hubei,China  2020-03-12  \n",
       "0    51553.0  30.9756  112.2707   NaN         13171.0  Hubei,China  2020-03-13  \n",
       "0    52960.0  30.9756  112.2707   NaN         11755.0  Hubei,China  2020-03-14  \n",
       "0    54288.0  30.9756  112.2707   NaN         10421.0  Hubei,China  2020-03-15  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cook county, IL, USA data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Last_Update</th>\n",
       "      <th>Confirmed</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-27 22:14:55</td>\n",
       "      <td>574.0</td>\n",
       "      <td>20.0</td>\n",
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       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-03-27</td>\n",
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       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-28 23:05:00</td>\n",
       "      <td>591.0</td>\n",
       "      <td>25.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>2020-03-28</td>\n",
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       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-29 23:08:00</td>\n",
       "      <td>646.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-03-29</td>\n",
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       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-30 22:52:00</td>\n",
       "      <td>848.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-03-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-31 23:43:56</td>\n",
       "      <td>890.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-03-31</td>\n",
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       "    <tr>\n",
       "      <th>1743</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-01 21:58:49</td>\n",
       "      <td>956.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
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       "      <td>6085.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-01</td>\n",
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       "    <tr>\n",
       "      <th>1808</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-02 23:25:00</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-02</td>\n",
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       "    <tr>\n",
       "      <th>1852</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-03 22:46:37</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-03</td>\n",
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       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-04 23:34:00</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-04</td>\n",
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       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-05 23:06:45</td>\n",
       "      <td>1207.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-06 23:22:00</td>\n",
       "      <td>1207.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2038</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-07 23:04:49</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2058</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-08 22:51:58</td>\n",
       "      <td>1380.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2081</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-09 23:02:37</td>\n",
       "      <td>1380.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-10 22:54:07</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-11 22:45:33</td>\n",
       "      <td>1484.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2142</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-12 23:18:00</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>1515.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-13 23:07:54</td>\n",
       "      <td>1666.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>1606.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2164</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-14 23:33:31</td>\n",
       "      <td>1666.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>1606.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2176</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-15 22:56:51</td>\n",
       "      <td>1793.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>1728.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2188</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-16 23:30:51</td>\n",
       "      <td>1833.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>1764.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2190</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-17 23:30:52</td>\n",
       "      <td>1870.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>1797.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2197</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-18 22:32:47</td>\n",
       "      <td>1870.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>1797.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2210</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-19 23:41:01</td>\n",
       "      <td>1870.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>1797.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2217</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-20 23:36:47</td>\n",
       "      <td>1922.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>1839.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2226</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-21 23:30:50</td>\n",
       "      <td>1922.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>1839.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2233</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-22 23:30:53</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>1868.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2247</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-24 03:30:50</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>1892.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-25 06:30:53</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2257</th>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-26 02:30:51</td>\n",
       "      <td>2040.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.231049</td>\n",
       "      <td>-121.697046</td>\n",
       "      <td>6085.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>1941.0</td>\n",
       "      <td>Santa Clara, California, US</td>\n",
       "      <td>2020-04-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Province_State Country_Region         Last_Update  Confirmed  Deaths  \\\n",
       "2529     California             US 2020-03-27 22:14:55      574.0    20.0   \n",
       "2529     California             US 2020-03-28 23:05:00      591.0    25.0   \n",
       "2529     California             US 2020-03-29 23:08:00      646.0    25.0   \n",
       "2529     California             US 2020-03-30 22:52:00      848.0    28.0   \n",
       "1701     California             US 2020-03-31 23:43:56      890.0    30.0   \n",
       "1743     California             US 2020-04-01 21:58:49      956.0    32.0   \n",
       "1808     California             US 2020-04-02 23:25:00     1019.0    36.0   \n",
       "1852     California             US 2020-04-03 22:46:37     1019.0    36.0   \n",
       "1898     California             US 2020-04-04 23:34:00     1148.0    39.0   \n",
       "1964     California             US 2020-04-05 23:06:45     1207.0    39.0   \n",
       "1997     California             US 2020-04-06 23:22:00     1207.0    39.0   \n",
       "2038     California             US 2020-04-07 23:04:49     1285.0    43.0   \n",
       "2058     California             US 2020-04-08 22:51:58     1380.0    46.0   \n",
       "2081     California             US 2020-04-09 23:02:37     1380.0    46.0   \n",
       "2100     California             US 2020-04-10 22:54:07     1442.0    47.0   \n",
       "2125     California             US 2020-04-11 22:45:33     1484.0    50.0   \n",
       "2142     California             US 2020-04-12 23:18:00     1566.0    51.0   \n",
       "2156     California             US 2020-04-13 23:07:54     1666.0    60.0   \n",
       "2164     California             US 2020-04-14 23:33:31     1666.0    60.0   \n",
       "2176     California             US 2020-04-15 22:56:51     1793.0    65.0   \n",
       "2188     California             US 2020-04-16 23:30:51     1833.0    69.0   \n",
       "2190     California             US 2020-04-17 23:30:52     1870.0    73.0   \n",
       "2197     California             US 2020-04-18 22:32:47     1870.0    73.0   \n",
       "2210     California             US 2020-04-19 23:41:01     1870.0    73.0   \n",
       "2217     California             US 2020-04-20 23:36:47     1922.0    83.0   \n",
       "2226     California             US 2020-04-21 23:30:50     1922.0    83.0   \n",
       "2233     California             US 2020-04-22 23:30:53     1962.0    94.0   \n",
       "2247     California             US 2020-04-24 03:30:50     1987.0    95.0   \n",
       "2252     California             US 2020-04-25 06:30:53     2018.0    98.0   \n",
       "2257     California             US 2020-04-26 02:30:51     2040.0    99.0   \n",
       "\n",
       "      Recovered        Lat       Long_    FIPS       Admin2  Active  \\\n",
       "2529        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "2529        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "2529        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "2529        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "1701        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "1743        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "1808        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "1852        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "1898        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "1964        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "1997        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "2038        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "2058        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "2081        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "2100        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "2125        0.0  37.231049 -121.697046  6085.0  Santa Clara     0.0   \n",
       "2142        0.0  37.231049 -121.697046  6085.0  Santa Clara  1515.0   \n",
       "2156        0.0  37.231049 -121.697046  6085.0  Santa Clara  1606.0   \n",
       "2164        0.0  37.231049 -121.697046  6085.0  Santa Clara  1606.0   \n",
       "2176        0.0  37.231049 -121.697046  6085.0  Santa Clara  1728.0   \n",
       "2188        0.0  37.231049 -121.697046  6085.0  Santa Clara  1764.0   \n",
       "2190        0.0  37.231049 -121.697046  6085.0  Santa Clara  1797.0   \n",
       "2197        0.0  37.231049 -121.697046  6085.0  Santa Clara  1797.0   \n",
       "2210        0.0  37.231049 -121.697046  6085.0  Santa Clara  1797.0   \n",
       "2217        0.0  37.231049 -121.697046  6085.0  Santa Clara  1839.0   \n",
       "2226        0.0  37.231049 -121.697046  6085.0  Santa Clara  1839.0   \n",
       "2233        0.0  37.231049 -121.697046  6085.0  Santa Clara  1868.0   \n",
       "2247        0.0  37.231049 -121.697046  6085.0  Santa Clara  1892.0   \n",
       "2252        0.0  37.231049 -121.697046  6085.0  Santa Clara  1920.0   \n",
       "2257        0.0  37.231049 -121.697046  6085.0  Santa Clara  1941.0   \n",
       "\n",
       "                     Combined_Key Update_Date  \n",
       "2529  Santa Clara, California, US  2020-03-27  \n",
       "2529  Santa Clara, California, US  2020-03-28  \n",
       "2529  Santa Clara, California, US  2020-03-29  \n",
       "2529  Santa Clara, California, US  2020-03-30  \n",
       "1701  Santa Clara, California, US  2020-03-31  \n",
       "1743  Santa Clara, California, US  2020-04-01  \n",
       "1808  Santa Clara, California, US  2020-04-02  \n",
       "1852  Santa Clara, California, US  2020-04-03  \n",
       "1898  Santa Clara, California, US  2020-04-04  \n",
       "1964  Santa Clara, California, US  2020-04-05  \n",
       "1997  Santa Clara, California, US  2020-04-06  \n",
       "2038  Santa Clara, California, US  2020-04-07  \n",
       "2058  Santa Clara, California, US  2020-04-08  \n",
       "2081  Santa Clara, California, US  2020-04-09  \n",
       "2100  Santa Clara, California, US  2020-04-10  \n",
       "2125  Santa Clara, California, US  2020-04-11  \n",
       "2142  Santa Clara, California, US  2020-04-12  \n",
       "2156  Santa Clara, California, US  2020-04-13  \n",
       "2164  Santa Clara, California, US  2020-04-14  \n",
       "2176  Santa Clara, California, US  2020-04-15  \n",
       "2188  Santa Clara, California, US  2020-04-16  \n",
       "2190  Santa Clara, California, US  2020-04-17  \n",
       "2197  Santa Clara, California, US  2020-04-18  \n",
       "2210  Santa Clara, California, US  2020-04-19  \n",
       "2217  Santa Clara, California, US  2020-04-20  \n",
       "2226  Santa Clara, California, US  2020-04-21  \n",
       "2233  Santa Clara, California, US  2020-04-22  \n",
       "2247  Santa Clara, California, US  2020-04-24  \n",
       "2252  Santa Clara, California, US  2020-04-25  \n",
       "2257  Santa Clara, California, US  2020-04-26  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jhs_data[(jhs_data['Country_Region'] == 'US') & (jhs_data['Province_State'] == 'California') & (jhs_data['Admin2'] == 'Santa Clara')].tail(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Last_Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-02 23:25:27</td>\n",
       "      <td>5575.0</td>\n",
       "      <td>107.0</td>\n",
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       "      <td>Cook, Illinois, US</td>\n",
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       "      <th>484</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-03 22:46:37</td>\n",
       "      <td>6111.0</td>\n",
       "      <td>141.0</td>\n",
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       "      <td>17031.0</td>\n",
       "      <td>Cook</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cook, Illinois, US</td>\n",
       "      <td>2020-04-03</td>\n",
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       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-04 23:34:21</td>\n",
       "      <td>7439.0</td>\n",
       "      <td>167.0</td>\n",
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       "      <td>41.841448</td>\n",
       "      <td>-87.816588</td>\n",
       "      <td>17031.0</td>\n",
       "      <td>Cook</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cook, Illinois, US</td>\n",
       "      <td>2020-04-04</td>\n",
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       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-05 23:06:45</td>\n",
       "      <td>8034.0</td>\n",
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       "      <td>17031.0</td>\n",
       "      <td>Cook</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cook, Illinois, US</td>\n",
       "      <td>2020-04-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-04-06 23:22:15</td>\n",
       "      <td>8728.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.841448</td>\n",
       "      <td>-87.816588</td>\n",
       "      <td>17031.0</td>\n",
       "      <td>Cook</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cook, Illinois, US</td>\n",
       "      <td>2020-04-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province_State Country_Region         Last_Update  Confirmed  Deaths  \\\n",
       "478       Illinois             US 2020-04-02 23:25:27     5575.0   107.0   \n",
       "484       Illinois             US 2020-04-03 22:46:37     6111.0   141.0   \n",
       "495       Illinois             US 2020-04-04 23:34:21     7439.0   167.0   \n",
       "510       Illinois             US 2020-04-05 23:06:45     8034.0   186.0   \n",
       "513       Illinois             US 2020-04-06 23:22:15     8728.0   209.0   \n",
       "\n",
       "     Recovered        Lat      Long_     FIPS Admin2  Active  \\\n",
       "478        0.0  41.841448 -87.816588  17031.0   Cook     0.0   \n",
       "484        0.0  41.841448 -87.816588  17031.0   Cook     0.0   \n",
       "495        0.0  41.841448 -87.816588  17031.0   Cook     0.0   \n",
       "510        0.0  41.841448 -87.816588  17031.0   Cook     0.0   \n",
       "513        0.0  41.841448 -87.816588  17031.0   Cook     0.0   \n",
       "\n",
       "           Combined_Key Update_Date  \n",
       "478  Cook, Illinois, US  2020-04-02  \n",
       "484  Cook, Illinois, US  2020-04-03  \n",
       "495  Cook, Illinois, US  2020-04-04  \n",
       "510  Cook, Illinois, US  2020-04-05  \n",
       "513  Cook, Illinois, US  2020-04-06  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jhs_data[(jhs_data['Country_Region'] == 'US') & (jhs_data['Province_State'] == 'Illinois') & (jhs_data['Admin2'] == 'Cook')].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot New York State Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1790db5b7b8>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ny_data = jhs_data[(jhs_data['Country_Region'] == 'US') & (jhs_data['Province_State'] == 'New York')]\n",
    "frm = ny_data[['Last_Update', 'Confirmed']].groupby('Last_Update').sum()\n",
    "frm.plot(y='Confirmed', logy=True, grid=True, style='*:')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check Italy Data (no province and state info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Last_Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long_</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Admin2</th>\n",
       "      <th>Active</th>\n",
       "      <th>Combined_Key</th>\n",
       "      <th>Update_Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2472</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-04-02 23:25:14</td>\n",
       "      <td>115242.0</td>\n",
       "      <td>13915.0</td>\n",
       "      <td>18278.0</td>\n",
       "      <td>41.87194</td>\n",
       "      <td>12.56738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83049.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-04-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2528</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-04-03 22:46:20</td>\n",
       "      <td>119827.0</td>\n",
       "      <td>14681.0</td>\n",
       "      <td>19758.0</td>\n",
       "      <td>41.87194</td>\n",
       "      <td>12.56738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85388.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-04-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2582</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-04-04 23:34:04</td>\n",
       "      <td>124632.0</td>\n",
       "      <td>15362.0</td>\n",
       "      <td>20996.0</td>\n",
       "      <td>41.87194</td>\n",
       "      <td>12.56738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88274.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2665</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-04-05 23:06:26</td>\n",
       "      <td>128948.0</td>\n",
       "      <td>15887.0</td>\n",
       "      <td>21815.0</td>\n",
       "      <td>41.87194</td>\n",
       "      <td>12.56738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91246.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-04-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2709</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-04-06 23:21:55</td>\n",
       "      <td>132547.0</td>\n",
       "      <td>16523.0</td>\n",
       "      <td>22837.0</td>\n",
       "      <td>41.87194</td>\n",
       "      <td>12.56738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93187.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-04-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Province_State Country_Region         Last_Update  Confirmed   Deaths  \\\n",
       "2472            NaN          Italy 2020-04-02 23:25:14   115242.0  13915.0   \n",
       "2528            NaN          Italy 2020-04-03 22:46:20   119827.0  14681.0   \n",
       "2582            NaN          Italy 2020-04-04 23:34:04   124632.0  15362.0   \n",
       "2665            NaN          Italy 2020-04-05 23:06:26   128948.0  15887.0   \n",
       "2709            NaN          Italy 2020-04-06 23:21:55   132547.0  16523.0   \n",
       "\n",
       "      Recovered       Lat     Long_  FIPS Admin2   Active Combined_Key  \\\n",
       "2472    18278.0  41.87194  12.56738   NaN    NaN  83049.0        Italy   \n",
       "2528    19758.0  41.87194  12.56738   NaN    NaN  85388.0        Italy   \n",
       "2582    20996.0  41.87194  12.56738   NaN    NaN  88274.0        Italy   \n",
       "2665    21815.0  41.87194  12.56738   NaN    NaN  91246.0        Italy   \n",
       "2709    22837.0  41.87194  12.56738   NaN    NaN  93187.0        Italy   \n",
       "\n",
       "     Update_Date  \n",
       "2472  2020-04-02  \n",
       "2528  2020-04-03  \n",
       "2582  2020-04-04  \n",
       "2665  2020-04-05  \n",
       "2709  2020-04-06  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jhs_data[jhs_data['Country_Region'] == 'Italy'].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Testing Data From CovidTracking Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import urllib.request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_states_current = 'https://covidtracking.com/api/states'\n",
    "url_states_daily = 'https://covidtracking.com/api/states/daily'\n",
    "test_daily = urllib.request.urlopen(url_states_daily).read().decode()\n",
    "data_json = json.loads(test_daily)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(url_states_daily + '.csv')\n",
    "data['date'] = pd.to_datetime([str(d) for d in data['date']])\n",
    "data = data.sort_values(by='date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>pending</th>\n",
       "      <th>hospitalizedCurrently</th>\n",
       "      <th>hospitalizedCumulative</th>\n",
       "      <th>inIcuCurrently</th>\n",
       "      <th>inIcuCumulative</th>\n",
       "      <th>onVentilatorCurrently</th>\n",
       "      <th>onVentilatorCumulative</th>\n",
       "      <th>...</th>\n",
       "      <th>total</th>\n",
       "      <th>totalTestResults</th>\n",
       "      <th>posNeg</th>\n",
       "      <th>fips</th>\n",
       "      <th>deathIncrease</th>\n",
       "      <th>hospitalizedIncrease</th>\n",
       "      <th>negativeIncrease</th>\n",
       "      <th>positiveIncrease</th>\n",
       "      <th>totalTestResultsIncrease</th>\n",
       "      <th>dailyPositiveRate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-03</th>\n",
       "      <td>IL</td>\n",
       "      <td>61499.0</td>\n",
       "      <td>257814.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4701.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>759.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>319313</td>\n",
       "      <td>319313</td>\n",
       "      <td>319313</td>\n",
       "      <td>17</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16423.0</td>\n",
       "      <td>2994.0</td>\n",
       "      <td>19417.0</td>\n",
       "      <td>0.154195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-04</th>\n",
       "      <td>IL</td>\n",
       "      <td>63840.0</td>\n",
       "      <td>269307.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4493.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>763.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>333147</td>\n",
       "      <td>333147</td>\n",
       "      <td>333147</td>\n",
       "      <td>17</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11493.0</td>\n",
       "      <td>2341.0</td>\n",
       "      <td>13834.0</td>\n",
       "      <td>0.169221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-05</th>\n",
       "      <td>IL</td>\n",
       "      <td>65962.0</td>\n",
       "      <td>280324.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1266.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>346286</td>\n",
       "      <td>346286</td>\n",
       "      <td>346286</td>\n",
       "      <td>17</td>\n",
       "      <td>176.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11017.0</td>\n",
       "      <td>2122.0</td>\n",
       "      <td>13139.0</td>\n",
       "      <td>0.161504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06</th>\n",
       "      <td>IL</td>\n",
       "      <td>68232.0</td>\n",
       "      <td>293028.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4832.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>361260</td>\n",
       "      <td>361260</td>\n",
       "      <td>361260</td>\n",
       "      <td>17</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12704.0</td>\n",
       "      <td>2270.0</td>\n",
       "      <td>14974.0</td>\n",
       "      <td>0.151596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07</th>\n",
       "      <td>IL</td>\n",
       "      <td>70873.0</td>\n",
       "      <td>308170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4862.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1253.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>766.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>379043</td>\n",
       "      <td>379043</td>\n",
       "      <td>379043</td>\n",
       "      <td>17</td>\n",
       "      <td>137.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15142.0</td>\n",
       "      <td>2641.0</td>\n",
       "      <td>17783.0</td>\n",
       "      <td>0.148513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           state  positive  negative  pending  hospitalizedCurrently  \\\n",
       "date                                                                   \n",
       "2020-05-03    IL   61499.0  257814.0      NaN                 4701.0   \n",
       "2020-05-04    IL   63840.0  269307.0      NaN                 4493.0   \n",
       "2020-05-05    IL   65962.0  280324.0      NaN                 4780.0   \n",
       "2020-05-06    IL   68232.0  293028.0      NaN                 4832.0   \n",
       "2020-05-07    IL   70873.0  308170.0      NaN                 4862.0   \n",
       "\n",
       "            hospitalizedCumulative  inIcuCurrently  inIcuCumulative  \\\n",
       "date                                                                  \n",
       "2020-05-03                     NaN          1232.0              NaN   \n",
       "2020-05-04                     NaN          1232.0              NaN   \n",
       "2020-05-05                     NaN          1266.0              NaN   \n",
       "2020-05-06                     NaN          1231.0              NaN   \n",
       "2020-05-07                     NaN          1253.0              NaN   \n",
       "\n",
       "            onVentilatorCurrently  onVentilatorCumulative  ...   total  \\\n",
       "date                                                       ...           \n",
       "2020-05-03                  759.0                     NaN  ...  319313   \n",
       "2020-05-04                  763.0                     NaN  ...  333147   \n",
       "2020-05-05                  780.0                     NaN  ...  346286   \n",
       "2020-05-06                  780.0                     NaN  ...  361260   \n",
       "2020-05-07                  766.0                     NaN  ...  379043   \n",
       "\n",
       "           totalTestResults  posNeg fips deathIncrease  hospitalizedIncrease  \\\n",
       "date                                                                           \n",
       "2020-05-03           319313  319313   17          59.0                   0.0   \n",
       "2020-05-04           333147  333147   17          44.0                   0.0   \n",
       "2020-05-05           346286  346286   17         176.0                   0.0   \n",
       "2020-05-06           361260  361260   17         136.0                   0.0   \n",
       "2020-05-07           379043  379043   17         137.0                   0.0   \n",
       "\n",
       "            negativeIncrease  positiveIncrease  totalTestResultsIncrease  \\\n",
       "date                                                                       \n",
       "2020-05-03           16423.0            2994.0                   19417.0   \n",
       "2020-05-04           11493.0            2341.0                   13834.0   \n",
       "2020-05-05           11017.0            2122.0                   13139.0   \n",
       "2020-05-06           12704.0            2270.0                   14974.0   \n",
       "2020-05-07           15142.0            2641.0                   17783.0   \n",
       "\n",
       "            dailyPositiveRate  \n",
       "date                           \n",
       "2020-05-03           0.154195  \n",
       "2020-05-04           0.169221  \n",
       "2020-05-05           0.161504  \n",
       "2020-05-06           0.151596  \n",
       "2020-05-07           0.148513  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frm = data[data['state']=='IL'].set_index('date')   # one state\n",
    "#frm = pd.pivot_table(data, index='date', aggfunc='sum')  # whole contry\n",
    "frm['dailyPositiveRate'] = frm['positiveIncrease'] / frm['totalTestResultsIncrease']\n",
    "frm.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e0191107b8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Neno6n3/umB9UUFBAbm4u11xzDc8884x79nHlezX2rGQhhDDS6NGj+fDDDykqKiI/P5///Oc/gGPduk6dOlFWVsZbb71V53U6d+5M586dWbRoEbNmzarznq5rHjx4kNTUVOLi4qr9fvFU0/cXOJLVqVOn8tvf/pZ+/foRGRkJwMSJE/jo7dfclRgO7ttNm9AgOrU+35AycuRI7rjjDv7v//7PndS1b9+egoKCCrOKPe8fFxdHZmamO9ErKytj7969df6cPJiUUikejzn1ORkvi0tUuWk9b9KivHjH+cUMF10/sJYjvRMZGcmoUaMYOHAgV199NQkJCSRddhm9evVixoIFdOzYkcjISEwmE4mJicyaNYt58+Zx44038t577zFu3DjCwsLqvE///v1ZtGgREyZMwG63Yzabee655wgJCWH27NnuljZXy9asWbOYO3cuISEhVf4nEhISwu9+9zsWL17MsmXL+M1vfkNCQgJaa2JjY/nkk09YtWoVK1aswGw207FjRx577DHMZrN7EkSPHj3o27dvtbHOmTOHq6++mk6dOvHMM89UG583Fi5cyH333cfAgQMJDAzk8ccfrzLwt6afy4gRIxg0aBADBgygZ8+ejBo1CnB8qE2ZMoXi4mK01u7JIUuXLuW+++4jISGB8vJyRo8ezQsvvOB1rEII4c8GDx7MtGnTSEpKonv37lx++eUA/PnPf+bSSy+le/fuxMfH15hgeZo+fTqZmZn079+/1uPmzZvH3LlziY+Px2QysXz5coKDg6v9fvGUkJBQ4Ttz0KBBFfZPmzaNYcOGsXz5cve2Zc8+y533zOXGK0dhs9kYfOlIRg4bQmBAxbatRx55hMGDB/P73/+ee+65h/j4eGJjY93Df6Dq9+fq1at54IEHyM3Npby8nN/85jcMGFB3D6JTudb6QlZRrqkgRa1UTc2p/s5isejKTav79++nX79+BkUkLkbyb65xSGUMcTFpSZ8b8+fPZ9CgQdx1112Nfu0LGRt9PKuQvKJyNJoIixmloHtk3Q0pjaW6v2OllFVrXWsQzjF6n2itq7Q+KaWuBebjmJRxKbBUaz28rlgu6hY9n3nvPejaFUaMMDoSIYQQolEMGTKEsLAw/vnPfxodShVd2oSQW5QHQJtQM21CgwyOqG5KqXeAsUB7pVQa8DhgBtBavwB8hiPJOwxYgdneXFcSPV+YPx+mTpVETwghRIuxfft2o0OoUWn5+cmCZbbm0XOptb61jv0auK++15VEzxc2bXLMlBJCCCFEkyu1nU/0yj2eX4wk0fOFnj2NjkAIIYQfqWnJKNE4SpwteubAAJ+36Pnb3IeLenkVn/nPf+Czz4yOQgghhB+wWCxkZWX5XULQkpSW2zEHBhAUGECZ3XctelprsrKyvFoT11ekRc8X/v53sFjgmmuMjkQIIYTBYmJiSEtLIzMz0+hQ/FpxcXGDE6bMfMe6uIEBitJyO6VnfZd4WSwWYmJifHa/ukii5wurVztWO/fSCy+8QGhoKDNmzGD58uVMmDCBzp07A3D33Xfz29/+ts41i6oTGxtLSkpKhdXKhRBC+JbZbKZHjx5Gh+H3kpOTq6yb561Zf/mKy3t3oE2Imbe2pLLvTxMv2q5ySfTyz8Dq2XDTcgiPbpp7eFmSxmXu3Lnu58uXL2fgwIHuRO+VV15p1NDqy2azuUuOCSGEEP6muMxGel4J3duFYjEHUlRmI7+knAiL9w0uLYmM0fvmH5C6Gb75e6Nc7tixY/Tt25eZM2eSkJDATTfdRPGnn7L3kUcYNGgQ8fHx3HnnnZSUOJqVFyxYQP/+/UlISODBBx8E4IknnmDx4sWsXr2alJQUpk+fTlJSEkVFRYwdO5aUlBSef/55Hn74Yfd9ly9f7i4YvWLFCoYPH05SUhL33ntvlbJqx44do1+/ftxzzz0MGDCACRMmUFRUBMDhw4e54oorSExMZPDgwRw5coTk5GTGjRvHbbfdRnx8PDabjYceeohhw4aRkJDAiy++CDhKio0fP57BgwcTHx/Pxx9/DEBhYSHXXnstiYmJDBw4kFWrVgGOqfljxoxhyJAhTJw4kdOnay3XJ4QQQtTpRLYVgG6RoURFOGqnZ+TVXY+9pWq5LXqfL4Azu2ven7oRPAfCprzqeCgF3UZVf07HeLj6b3Xe+sCBA7z66quMGjWKO++8kwN//CPtd+1i1b599OnThxkzZvD8888zY8YMPvzwQ3766SeUUpw7d67CdW666SaWLVvG4sWLGTp0aJV9I0eO5B//+AcAq1at4g9/+AP79+9n1apVbNy4EbPZzLx583jrrbeYMWNGhfMPHTrEO++8w8svv8wtt9zC+++/z+2338706dNZsGABU6dOpbi4GLvdzokTJ9i6dSt79uyhR48evPTSS7Ru3Zpt27ZRUlLCqFGjmDBhAl27duXDDz8kIiKCs2fPMmLECCZPnsx///tfOnfuzKeffgo46vuWlZVx//338/HHH9OhQwd3/K+99lqdP18hhBCiJseznIleu1D37NszuSX0impYlY3mruUmenXpPAxyjkJRFmg7qAAIjYS2Fz5uomvXru56qrfffjt/OHgQ+7BhfNanDwAzZ87kueeeY/78+VgsFu6++26uvfZaJk2a5PU9OnToQM+ePdm8eTO9e/fmwIEDjBo1iueee47t27e7a/UVFRURFRVV5fwePXqQlJQEOFY3P3bsGPn5+Zw8eZKpU6cCVBgEO3z4cPeYkrVr17Jr1y534efc3FwOHTpETEwMv//97/n2228JCAjg5MmTpKenEx8fz4MPPsgjjzzCpEmTuPzyy9mzZw979uzhyiuvBBxdwp06darXz1kIIYSo7LizRa97ZBi5RWUApEuLXgvkRcsb//lf+GE5mCxgK4V+k2HS0xd868oDPgvNZmwBVXvJTSYTW7duZd26daxcuZJly5bx9ddfe32fadOm8e6779K3b1+mTp2KUgqtNTNnzuSvf/1rrecGBwe7nwcGBlJUVFTrVP+wsPPl+bTWPPvss0ycOLHCMcuXLyczM5Pt27djNpuJjY2luLiYPn36sH37dj777DMeffRRJkyYwNSpUxkwYACbNm3y+v0KIYQQdUnNKiQ82ETbUDMWs+O7Nz3/4k30Lu4xeoUZMGQ23P2V48+C9Ea5bGpqqjuBeeedd5gRF8fEXbs4fOgQAG+++SZjxoyhoKCA3NxcrrnmGp555hl27NhR5Vrh4eHk5+dXe58bbriBjz76iHfeeYdp06YBMH78eFavXk1GRgYA2dnZHD9+3Ku4IyIiiImJ4aOPPgKgpKQEq9Va5biJEyfy/PPPU1bm+J/SwYMHKSwsJDc3l6ioKMxmM+vXr3ff99SpU4SGhnL77bfz4IMP8sMPPxAXF0dmZqb751RWVsbevXu9ilMIIYSoSWq2lW6RoSilCA0yEW4xkZFXYnRYhmm5LXre+OVb5583QkueS79+/Xj99de599576d27N9MTEgjKzWX4TTdRZLczbNgw5s6dS3Z2NlOmTKG4uBitNUuWLKlyrVmzZjF37lxCQkKqtH61bduW/v37s2/fPoYPHw5A//79WbRoERMmTMBut2M2m3nuuefo3r27V7G/+eab3HvvvTz22GOYzWbee++9KsfcfffdHDt2jMGDB6O1pkOHDnz00UdMnz6d6667jqFDh5KUlETfvn0B2L17Nw899BABAQGYzWaef/55goKCWL16NQ888AC5ubmUl5fzm9/8hgEDBtT3xy2EEEK4Hc+2Ehd9fjxedITlou66Vc11ZW6LxaKLiyv+xe3fv59+/foZFJHDsWPHmDRpEnv27Dm/sagIbDYIC3NM9hAthj/8m2sJkpOTGTt2rNFhCCH8SEM+F2x2Tb8//pfZl8Xy6NWOz+bpr2ymqNTGB/NqmGjpI0opq9Y6rO4jG9fF3XXrKyEh0KqVJHlCCCFEEzqTV0ypzU73dufzqehwC+kXcdetJHqNLDY2tmJrHsC+ffDkk5CdbUxQQgghxEUg1WNpFZeoCAsZ+cXY7c2zB/NCSaLnC3v3wsKFIAsCCyGEEE0mNbsQgO6R5xO9jhHBlNk0OdZSo8IyVIubjKG19r96dlOnQklJverdCv/XXMe3CiFES3U8y4opQNGp9fl1YKMjHM/T80qIbBVc06ktVotq0bNYLGRlZfnfF7DJBEFBMkavBdFak5WVVWFRaSGEEMZKzbYS0zYEU+D59CbKlegZuJZeRl4xBJoN+cJoUS16MTExpKWlkZmZaXQoFZjS02nz7rvkTZpEaY8Lr7wh/IPFYiEmJsboMIQQQjilZlvp6jE+DyDaWe82Pde4RO8vn+1HqWoqJ/hAi0r0zGazu0yXXykuhuefp8PEiSBLcQghhBBN4niWlesSK5bTjAo/33Xra3ELP3fX2zVKndmlUuo1pVSGUmqPx7ZVSqkdzscxpdQO5/ZYpVSRx74XPM4ZopTarZQ6rJRaqpwD6ZRSwc7rHVZKbVFKxTb+2zRYYqJjHb0pU4yORAghhGiRcq1l5BaVVVhaBSDIFEBkWJAhXbcbHh7H5KTOPr+vJ2+aEZcDV3lu0FpP01onaa2TgPeBDzx2H3Ht01rP9dj+PDAH6O18uK55F5Cjte4FLAH+3qB34s8CAhwPIYQQQjSJ1Gzn0iqRoVX2RUVYHOPkfCwqwkJQoLHj8+vMPrTW3wLVLgDnbJW7BXintmsopToBEVrrTdoxU+IN4Hrn7inA687nq4Hxyu+mzV4gqxV+/3vYsMHoSIQQQogW6bhzaZVu7aometERwYYtmnz0rCMB1eWlhgwSvNBmpsuBdK31IY9tPZRSPyqlvlFKXe7c1gVI8zgmzbnNte8EgNa6HMgFIi8wLv+iNTz1FKSkGB2JEEII0SIdr2axZBdHdQxjJmOMi+vgfKYNGax3oZMxbqVia95poJvWOkspNQT4SCk1AKiuhc61Bkpt+ypQSs3B0f2LyWQiOTm5oXH73pdfOv5sTjEL4SMFBQXN6/dZCNHk6vu5sGVvCRFBim2bvquyryinlMz8MtZ9vZ7AAN92Gq7bUUzHMMVxn971vAYnekopE3ADMMS1TWtdApQ4n29XSh0B+uBowfNchyIGOOV8ngZ0BdKc12xNDV3FWuuXgJcALBaLliLoQrQMDSleLoRo2er7ufDiwc306mhj7NhRVfalWY6z5sgeBgwZScfWvl3ObsH36xjRux1bfHrX8y6k6/YK4CettbtLVinVQSkV6HzeE8eki5+11qeBfKXUCOf4uxnAx87T1gAznc9vAr7WfrficSN47DFYvdroKIQQQogWKTXbSvfIsGr3dXRXx/Bt921GXjFn8oqJj2nj0/t68mZ5lXeATUCcUipNKXWXc9cvqToJYzSwSym1E8fEirlaa1fr3K+AV4DDwBHgc+f2V4FIpdRh4LfAggt4P/7r7bdh40ajoxBCCCFanJJyG6dyi6odnweeZdB8m+jtPpkLQEJMa5/e11OdXbda61tr2D6rmm3v41hupbrjU4CB1WwvBm6uK45m7/BhoyMQQgghWqS0nCK0hu7VLK0CHtUx8n0783ZXWi4BCvp3ivDpfT3J4m5CCCGEaNbca+jV0KIX2SqYAIXP19LbfTKXXlGtCAs2rhCZJHq+8s9/wtKlRkchhBBCtDipWTUvlgwQGKDoEB7MGR/Wu9Vasystl/guxo3PA0n0fGf9evj2W6OjEEIIIVqc41lWQoMC6dAquMZjOkZYfNp1eyavmLMFJYaOz4MLX0dPeOuTT4yOQAghhGiRUrOtdGsXSm2FtaIiLJxwdvH6wq40x0SMeIMTPWnRE0IIIUSzlppdSNcaxue5OMqg+a7rdndaLoEBytCJGCCJnu+89ho8/LDRUQghhBAtitbasYZeXYleuIUcaxkl5TafxLXrZC59osOxmAN9cr+aSKLnK3v2yBg9IYQQopFl5JdQXGavcWkVF9daehl5TT9OT2vN7rRzJHQxttsWZIye7zz9tNERCCGEEC2Oa2mVOrtuW59fNLmuYy9UWk4ROdYyw8fngbToCSGEEKIZO+5cWqWm8mcu7kWTfdCi5w8VMVwk0fOVjz+G226DFljGVwghhDBKalYhAQq6tAmp9Vp3NcwAACAASURBVLjocN+VQduVlos5UBHXMbzJ71UXSfR85eRJ2LYNSkuNjkQIIUQzkpFXzC0vbiIj37dVHZqL49lWOrcJIchUe0rTJtRMUGAA6T74Oe4+eY6+HSMINhk7EQMk0fOdefPg0CEIrnkxRyGEEKKypesOse1YNku/OmR0KH7JtYZeXZRSREUEN/lkDHdFDD/otgWZjCGEEEL4pbiFn1NSbne/XrEllRVbUgk2BXBg0dUGRuZfUrOsTBgQ7dWx0RGWJi+DdjzLSn5xuV/MuAVp0fOdjRvhppscXbhCCCFEHTY8PI5rBnZ0v7aYA5iS1JkNj4wzMCr/UlBSTlZhKd3a1T4Rw8VRBq1pE71dJ/2jIoaLJHq+kp8PP/0EVt+VXxFCCNF8RUVYMAU6SnoFKCgptxMebCLKOalAwPGsQoA619Bz8UXX7e60cwSZAugTbfxEDJCuW9+56irHQwghhPCSKynp1i6Uy3p3IFMmZFTgql3rzRg9cHTdFpSUU1BSTqvgpkmBdqXl0r9TBOZA/2hLk0RPCCGE8FNzxvRk89FszhaU8ucpA1BKGR2SX3GtodfNyxY911p6GXnFtOrQqtHjsds1e07mcuOQmEa/dkP5R7p5MTh8GCZPhq1bjY5ECCFEM5FdWAY4xqLlWMsMjsb/HM+20jbUTITF7NXxrrX0zjTRWno/ny2ksNRGvJ9MxABJ9HzHZoMTJ6Cw0OhIhBBCNBM5hefXXnWV+hLnpWZ5t7SKi6sMWlON09t98hwACTFtmuT6DSFdt74SFwc//mh0FEIIIZqRbGvFRC+pq/8kEP4gNdtKYj1+JtERTVsdY1daLiHmQHpFNX63cENJi54QQgjhp3IKSwl3Tho4IS16FZTZ7Jw8V0T3erTotQo2ERYU2GT1bnen5TKwSwSBAf4zllISPV8pKHDMun3/faMjEUII0UzkWEvp3CaE9q2CJdGr5NS5Imx27fVEDJfoJlpLr9xmZ++pPOK7NLzVVSl1lVLqgFLqsFJqQTX7Wyul/qOU2qmU2quUml3XNaXr1ldMJsjJgZKmXb9HCCFEy5FTWEbbMDOhwYEyRq+S1HoureLiWEuv8RO9I5mFFJXZSGjgQslKqUDgOeBKIA3YppRao7Xe53HYfcA+rfV1SqkOwAGl1Fta69JqLglIouc7Fgts2WJ0FEIIIZqRbGspfaJbYQ4MYPvxHKPD8SuupVW8XSzZJTrCwg+pjf+z3JXmmIhxARUxhgOHtdY/AyilVgJTAM9ETwPhyrHOTisgGyiv7aLSdSuEEEL4qZzCUtqGBtGtXSinc4sps9nrPukikZptJcgU4F4yxVsdIyyk55WgtW7UeHafzKVVsIkekd6VY6tGF+CEx+s05zZPy4B+wClgN/BrrXWt/ygk0fOlqVPh6aeNjkIIIUQzYLdrcqyltAsLomu7UGx2zelzUhnD5XhWId3ahRJQz4kPUREWSsvt5BY17rqEu5wTMWqJx6SUSvF4zKm0v7oTK2ejE4EdQGcgCVimlIqoLS5J9IQQQgg/lFdchl1Dm9AgurZ1dE/KOL3zUrOL6j0+D85Xx2jMmbdlNjv7TufVtX5eudZ6qMfjpUr704CuHq9jcLTceZoNfKAdDgNHgb613VTG6PnShx8aHYEQQohmItu5WHK7MLN7ZumJHEn0ANJzizhwJo/4LrU2ZlXLcy29uI7hjRLPwfR8SsvtF1oRYxvQWynVAzgJ/BK4rdIxqcB4YINSKhqIA36u7aKS6AkhhBB+KMe5WHLb0CA6RlgwBypp0XN6au0B7Bp+zqx/tSnXmL7GXDR5V1ouQINn3AJorcuVUvOBL4BA4DWt9V6l1Fzn/heAPwPLlVK7cXT1PqK1PlvbdetM9JRSrwGTgAyt9UDntieAe4BM52G/11p/5tz3KHAXYAMe0Fp/4dw+BFgOhACf4RhAqJVSwcAbwBAgC5imtT7mzQ+l2bn3XjCbYdkyoyMRQgjh51x1btuFBREYoIhpG3rRJ3pxCz+npPz83IOU4znELviUYFMABxZd7dU1otxdt42b6EVYTA3qSvbkzKU+q7TtBY/np4AJ9bmmN2P0lgNXVbN9idY6yflwJXn9cTQ1DnCe8y/nujAAzwNzgN7Oh+uadwE5WutewBLg7/V5A81KWJjjIYQQQtTBVee2bWgQADFtQy76RZM/feByYtqGuF9bzAFMSerMhkfGeX0NizmQNqHmRh2jt/vkORJi2uBY9cS/1Jnoaa2/xbFOizemACu11iVa66PAYWC4UqoTEKG13qQd85nfAK73OOd15/PVwHjljz+pxvD00/D3lpvHCiGEaDyurtt2YY5Er1u70Is60fvmYCa3v7KFtJwiAIJNAZSU2wkPNhFVzyVWosMtjdail5pdyN6TeVzSwT8bci5kjN58pdQMIAX4ndY6B8d6L5s9jnGtAVPmfF55O3isG+Psn84FIoFa+5yFEEKIlizbWkqQKYDQIEfHWLd2oeRYy8grLiPCYjY4Ot8pKCnnyU/3887WVHpFtWJE+3b0igrntuHdeHtrKpkNKGcWFRFMen7jtOj95bOf0MDRs/UfL+gLDU30nscxIFA7//wncCc1rwFT29ow3qwb4zjQsebMHACTyURycnK9gjZa7PLlhO/fz25p1ROigoKCgmb3+yxEU9t3uISwQM0333wDQN4ZRwGED9d+S/eIwNpObfbOFdtZ9oOVbWe+YtWBUrKKNFfFmrmht52gwBKghIyDZ7miDdCGen9+aGsJJ7JsF/S5c8/aQso8lir+9tBZYhd8ijkAXp7gP617DUr0tNbprudKqZeBT5wva1oDJs35vPJ2z3PSlFImoDU1dBU715x5CcBiseixY8c2JHzj7NoFgYE0u7iFaGLJycnyeyFEJSuOp9DRXsTYsZcD0P5kLs/t+I7onv0ZO7CTwdE1rQXv7+Jw3gkO7yghNjKUF2YmMjS2XaNdf1vJT2z65mcuHz2GwHouuOyycXAxiz7bzyc7T2HXjvGCEwd05A/X9qt3V3JTalCip5TqpLU+7Xw5FdjjfL4GeFsp9TSOVZt7A1u11jalVL5SagSwBZgBPOtxzkxgE3AT8LVu7Lok/uKBB4yOQAghRDPhqIpxvou2q3NG54nsIqNCanKVZ9UCHMuyMv2VLV7PqvVGxwgLNrsmq7CkwUlZVISFoECFXUNggGrweMGmVudkDKXUOziSsDilVJpS6i7gH0qp3UqpXcA44H8BtNZ7gXdxFOD9L3Cf1trmvNSvgFdwTNA4Anzu3P4qEKmUOgz8FljQWG9OCCGEaK5yCktp45xxC9A6xEzrEHOLXmJlw8PjGNkz0v26IbNqvRHlXDQ54wJn3h44UwDA4psTmX5pdzILGm8mb2Ops0VPa31rNZtfreX4J4Enq9meAgysZnsxcHNdcbQIL74IS5bA3r0Q2LLHVwghhLgw2dZS2nkkegBd24UYmuhl5BUz/50fWXbboCZpuYqKOD8b1hxAk7WSeVbHGHgB1Sxi2oaQnlfMlMTOTB3Upe4TDCC1bn2pQwdITITycqMjEUII4cfKbXZyi8poG1Yx0TN6iZWl6w6x7Vg2S786VO3+jLxibnlxExkNmAkLUFxmIzXbSs/2YfxxhKXJWskao95tcZmNbw5mcmX/aAIaOM7PF6QEmi/dcIPjIYQQQtQit6gMraFdaMVlVLq2C+WrfRnY7dqnyUXlsXMrtqSyYksqpgDFyjkjiG0fRmRYUIVEcNHU+HrfJ/lABuV2zf+bMgDbyb3MGFulI7BRtG8VjFIXVh3j+yNnsZbamDCgYyNG1vgk0RNCCCH8jLvObTUteqU2O+n5xXRqHVLdqU1iw8PjmPNmCjtOOGq6KhzroJXbNTe9sKnK8a5EsD6lyQA+3nGK9q2CGdkzku9ONlLw1TAHBtC+VfAFJXpr96bTKtjEiJ6NNxu4KUjXrS99+in07AmHDxsdiRBCCD+WYz1f59ZT17aOmbepWb7tvo2KsJCZ70g+g00BoOC24V1Z/+BY/j1rGP97RW96tA/F1choDlT1nkSRV1zGup8ymJTQCVNg06cn0RENT/Rsds1X+9MZG9eBYJN/j7mXFj1f6tABRo2C4GCjIxFCCOHHsivVuXXp5lxiJTXbyqUes1ObWnGZjdO5RcRFt2LJtEHuihQ92ofRo30Y4/pGkZFfwrGsVADKbLrekyj+u+cMpeV2rvfRpIbocAuncxuW6O04kcPZglKu7B/dyFE1Pkn0fGn4cHjzTaOjEEII4edyCivWuXXp3CaEAAUncny7lt7Gw2exa/jDtf3p3zmCRddXHTt3tqCE6Zd2JzzYxPPfHOFQRn697rFmxym6R4aSGNPwWbD1ERVhYWfauQadu3ZvOuZAxbi+UY0cVeOTRE8IIYTwM9nW6lv0gkwBdGod4vOZt1/tTycsKJBLaxmP9uIdQwGwlpbz9tZU2oV533uVkVfM90fOMn9cL5TyzSST6IhgzhaUUmazY65HV7HWmrX70hnRM7JZ1ByWMXq+tHs3xMTA2rVGRyKEEMKP5RSWYjEHEBJUdfyXr9fSs9s16/ZnMMbL8WihQSZuu7QbX+w94/VYwk92ncauYXJS5wsN12sdXYsm59dviZUjmQUcPVvIhGbQbQuS6PlWmzYwYQK0b290JEIIIfxYdmFZlcWSXXy9lt7uk7lk5Jcwvq/3ic3MkbEEKMVrG496dfzHO08xoHMEvaLCGxpmvXkumlwfX+xNB+AKSfREFV27wmuvweDBRkcihBDCj+VYS6ssreLSrV0oGfklFJXaqt3f2NbtTydAUa/xaB1bW5ic2Jl3U06QW1RW67FHzxay88Q5pviwNQ8gyrlockY9E721+9JJjGnt0+VtLoQkekIIIYSfyS4srTIRw6Wrc+ZtWo5vWvW+3J/B0O7taoynJnde1gNrqY2VW1NrPW7NjlMoBdcl+jbRc7Xo/fXzn7yu5JGeV8zOE+eaxWxbF0n0fCkvD6Kj4V//MjoSIYQQfizHWlplIoZLV48lVprayXNF7D+dx/h+9Z9dOrBLa0b2jGT598cos9mrPUZrzcc7T3Jpj3Y+byFrFxqEAo5nWWss6VbZl/sc3bb+Xg3DkyR6vhQSAlOnQp8+RkcihBDCj9XWoudaS88X4/TW7b+w8Wj3jO7B6dxiPtt9utr9e0/l8XNmIVOSfLN2nkvcws/p+fvP0M7XK7akErvgU+IWfl7reV/uSyc2MpTeUa2aPshGIomeL5nN8MILcMUVRkcihBDCT5XZ7OQXl9fYohcZFkRoUCCp2U2/lt5X+zPo0T6MSzo0LLEZ2yeKnh3CeHnDz2itq+z/eMdJzIGKqwf6toVsw8PjmJzUmUBnKQ9vKnnkF5fx/ZGzXNk/2mdLwDQGSfSEEEIIP3LOXf6s+jXalFJ0axfa5F23BSXlbD6SxRUN6LZ1CQhQ3HVZD/aczGPL0ewK+2x2zZqdpxjTJ4o2NSS1TSUqwkJ4sAm71ijlqORRXGartZJH8oFMymy6WXXbgiR6vte7N/z610ZHIYQQwk/luBZLrmXyQ0zbpl9iZcPBTEptdsb3u7CJBzcOjqFtqJlXNlRcamXL0SzS80q4fpBvJ2G4uCp5vHP3CFoFB7L+QCZnaimJ9uW+dCLDghjcra0Po7xwkuj52q23OurdCiGEENVw1bmtaR09cK6ll2Ottju0sXy5P53WIWaGdr+wxMZiDuSOEd1Z91M6P2cWuLev2XGKsKDAeq3P15hevGMoi64fyIhLIvlg3ijMAYo5b6ZQXFZ12ZrScjvrf8pgfL8od3dvcyGJnq/96U9wyy1GRyGEEMJPuerc1tad2a1dCNZSG1nOYxubza5Z/1MG4+I6YKpHebCa3DEyFnNAgHsB5ZJyG5/tPs3EAR2rrf7ha32iw/m/Xw5i98lcHl69q0oCvfnnLPJLypnQv3l124IkekIIIYRfcdW5rW3dum6R9VtiJSOvmFte3OT1enE/pOaQYy1rtOoPHcKDuX5QZ1ZvTyOnsJRvDmSSV1zu05JndbmifzQPTohjzc5T/Cv5SIV9X+5LJ8QcyGW9m19lK0n0fG38eJg40egohBBC+KnzLXrVT8YA6Nq2fkusPPPVQbYdy/Z6vbiv9qdjClCM7tPBq+O9cddlPSkus/P21lQ+3nmKyLAgRvXyr8Rp3thLmJzYmcVrD7jXzNNa8+W+dEb3aY/FbHzrY32ZjA7gonPzzRDY/P6hCCGE8I3swjLCggJrTSpivEz04hZ+Tkn5+cWKV2xJZcWWVIJNARxYdHWN5321L50RPSOJsNScbNZXXMdwRvfpwKsbjpJjLeWmITGYG6FbuDEppfjHTQkcPVvIb1b+yIf3jaK4zMaZvGIe6h9ndHgN4l8/4YvB3Llwzz1GRyGEEMJP1Vbn1iUkKJCo8OA6u243PDyOqwZU7H6dOCC61vXijp4t5Ehm4QUtq1KTuy/rQba1FA3kFddeA9coFnMgL80YQmiwibtfT2H5xmMAJMS0NjawBpJEzwj26kvBCCGEELVVxfDkzVp6UREWTjuXDDE5Z4tuPZpNaFDNHXquahgXuqxKZXELP2fGa1vdr7/Ym+5VNQojdGodwot3DOFMXjEf/HgSgNe/P2ZsUA0kiZ6v3Xkn9OxpdBRCCCH81Lla6tx66toulBN1VMcot9n56Uw+HSOCWTP/Mn4R14Ecaxnz3/6B8hrqz365L52+HcPdNXUbi6saRbDJkXpYzAF1VqMw0q0vbaa0Ure3vyamtZFEz9cmT4YHHjA6CiGEEH4q2+pdi17XdqGczi2qkIxU9uW+dErK7fxpykD6d47gtdnD+cvUeJIPZPL4mr1VlhE5Zy0l5XgO45ug29ZVjaLUZifYFEBJuZ3wYFOt1SiM1NwS05rIZAxfu/56oyMQQgjhx3IKy7xq0evWLhS7hlPniohtH1btMa9vOkaXNiEVumFvu7QbJ3KsPJ98hO6RocwZfYl7X/KBTGx2zRWN3G3r4qpGcdvwbry9NZVML5d7MUJzS0xrIomer2kNxcUQEmJ0JEIIIfxMSbmNgpLyGuvceurW7vxaetUlegfO5LP552wWXN23SjWHhybEcSLbyl8++4kubUK5NqET4FhWpX2rYBJj2jTCu6nqxTuGup8vun5gk9yjMTWnxLQmkuj52pNPwh//CKWlYG68aetCCCGav3NWx0zU2qpiuHRt52gwOJFT/YSM1zcdI9gUwLShXavsCwhQLL45kTO5xfzvuzvo2DqY+C5t+OZAJtfEdyKgmZX5airNLTGtjiR6vvaLX4DJ5GjZE0IIITy469x6MUYvOtxCUGBAtTNvc4vK+PCHk0xO7FzjUi2OZUSGcsO/NnLPG9uZM7on+SXlDI29sNq2wr/IZAxf+5//gQULIKjuX2IhhBAXF1dVDG/G6AUEKGLahVS7aPLq7WkUldmY+T+xtV6jXVgQ/549HK01f/v8JwC2H8+pf+DCb9WZ6CmlXlNKZSil9nhse0op9ZNSapdS6kOlVBvn9lilVJFSaofz8YLHOUOUUruVUoeVUkuVUsq5PVgptcq5fYtSKrbx36YfsdkgN9fxpxBCCOHBmzq3nqpbS89u17y56RhDurdlYJe6F/m96plvybGeX7x45bYTzXIZEVE9b1r0lgNXVdr2JTBQa50AHAQe9dh3RGud5HzM9dj+PDAH6O18uK55F5Cjte4FLAH+Xu930ZysXg1t2sCBA0ZHIoQQws+4W/S8mIwBjpq3ldfS++ZQJseyrMwY2d2ra7SUZURE9epM9LTW3wLZlbat1VqXO19uBmJqu4ZSqhMQobXepB2L9rwBuNYZmQK87ny+Ghjvau1rkQYNgn/+Ezo0XqFoIYQQLUN2oaNlzZuuW3C06OUWlZHr0SL3xvfH6BAezNUDO3l1jZayjIioXmNMxrgTWOXxuodS6kcgD1iotd4AdAHSPI5Jc27D+ecJAK11uVIqF4gEzla+kVJqDo5WQUwmE8nJyY0QvgEGD4a9e42OQgi/UVBQ0Hx/n4VoRHsOlRBigo0bvvXq+Lx0R5vLh19+S2zrQNIL7SQfKGLyJWa+/867awDsP1rMuBgTY7uaSD5Rzr6jJ0lOzmrQe2gs8rnQOC4o0VNK/QEoB95ybjoNdNNaZymlhgAfKaUGANW10Lmmnda2r+JGrV8CXgKwWCx67NixFxC9QWw2yMmBVq3AIv9bEgIgOTmZZvn7LEQj+/DMj0QVnPP696HDqVye/fE7onr2Z2x8J/78yT4CA47x+2mjiY7w/jvG83Yz6hdyk5HPhcbR4Fm3SqmZwCRgurM7Fq11idY6y/l8O3AE6IOjBc+zezcGOOV8ngZ0dV7TBLSmUldxi7Jzp6Pbdu1aoyMRQgjhZ7ILvatz6+KqR3si24q1tJx3U05w1cCO9UryRMvWoERPKXUV8AgwWWtt9djeQSkV6HzeE8eki5+11qeBfKXUCOf4uxnAx87T1gAznc9vAr7WlYvvtSTdu8Ozz8LA5rnwohBCiKaT42WdW5cIi5m2oWZSs6189OMp8ovLmVXHkiri4lJn161S6h1gLNBeKZUGPI5jlm0w8KVz3sRm5wzb0cCflFLlgA2Yq7V2tc79CscM3hDgc+cD4FXgTaXUYRwteb9slHfmryIjYf58o6MQQgjhh3IKy+gTHV6vc1xLrGw/nkP/ThEM6S4LHovz6kz0tNa3VrP51RqOfR94v4Z9KUCVZiytdTFwc11xtBh2O5w54xijFxFhdDRCCCH8SHZhKe3q0XUL0D48mK/3Z6CBv98YT0teuELUn1TG8LXCQujSBV56yehIhBBC+JGiUhtFZbYaS5bV5My5YjQQFKiYnNilzuPFxUVq3fpaaCi8+CKMGGF0JEIIIfxITj2rYsQt/JyScrv7dalN0++x/xJsCuDAoqubJEbR/EiLnq8FBsKcOZCQYHQkQggh/Eh2PercQtWKFsEmqWghqpJEzwipqZCebnQUQggh/Eh9W/QqV7QotUlFC1GVJHpGGD4cHnvM6CiEEEL4kRxnGbN2Xta5BThbUML0S7vz4bxRTL+0O5kFJU0VnvABpdRVSqkDSqnDSqkFNRwzVim1Qym1Vyn1TV3XlDF6Rli2DLp2NToKIYQQfiSnnl23AC/eMdT9fNH1sj5rc+Zch/g54EocxSS2KaXWaK33eRzTBvgXcJXWOlUpFVXXdSXRM8JNNxkdgRBCCD+TXViKUtA6xPsWPdGiDAcOa61/BlBKrQSmAPs8jrkN+EBrnQqgtc6o66LSdWuE48fh6FGjoxBCCOFHcqyltA4xYwqUr+YWyqSUSvF4zKm0vwtwwuN1mnObpz5AW6VUslJqu1KqztLE0qJnhNtvh6AgWLfO6EiEEEL4ifrWuRXNTrnWemgt+6tb6bpySVgTMAQYj6PS2Cal1Gat9cGaLiqJnhGeeMKxzIoQQgjhlGMtpW2odNtexNIAzwH8McCpao45q7UuBAqVUt8CiUCNiZ60Dxth/HgYO9boKIQQQviR7MIyr5dWES3SNqC3UqqHUioI+CWwptIxHwOXK6VMSqlQ4FJgf20XlUTPCCdOwO7dRkchhBDCj+RI1+1FTWtdDswHvsCRvL2rtd6rlJqrlJrrPGY/8F9gF7AVeEVrvae260rXrREeewy++sqR8AkhhLjoaa3JtpZKi95FTmv9GfBZpW0vVHr9FPCUt9eURM8I998Pt95qdBRCCCH8hLXURmm5nbaS6IlGJomeEQYPNjoCIYQQfsRd/ky6bkUjkzF6Rjh1CjZuBF151rQQQoiLUU6ho/yZtOiJxiaJnhFefx0uuwxKS42ORAghhB/IdrXo1aPOrRDekK5bI9xyCwwZImvpCSGEABpW51YIb0iiZ4RLLnE8hBBCCBxVMUASPdH4pOvWCJmZsH49FBYaHYkQQgg/kGMtJUBBRIh03YrGJYmeEZKT4Re/gKNHjY5ECCGEH8guLKVNaBCBAdWVOxWi4STRM8KYMY4WvdhYoyMRQgjhB6TOrWgqMkbPCFFRjocQQgiBo0VPqmKIpiAtekbIy4O1ayE93ehIhBBC+IGcwjKZiCGahCR6Rvj5Z5g4Eb7/3uhIhBBC+IEcqXMrmoh03Rqhd2/47jvo18/oSIQQQhhMa+0YoyeJnmgCkugZISwMRo0yOgohhBB+oKCknDKbljq3oklI160Rysrgk0/g8GGjIxFCCGEwV53bNjLrVjQBSfSMUFoK110HH3xgdCRCCCEMdr7OrbToicZXZ6KnlHpNKZWhlNrjsa2dUupLpdQh559tPfY9qpQ6rJQ6oJSa6LF9iFJqt3PfUqWUcm4PVkqtcm7fopSKbdy36IdCQmDrVpg50+hIhBBCGMxd51YSPdEEvGnRWw5cVWnbAmCd1ro3sM75GqVUf+CXwADnOf9SSgU6z3kemAP0dj5c17wLyNFa9wKWAH9v6JtpNgICYNgwiI42OhIhhBAGc9W5lTF6oinUmehprb8FsittngK87nz+OnC9x/aVWusSrfVR4DAwXCnVCYjQWm/SWmvgjUrnuK61Ghjvau1r0T79FFJSjI5CCCGEwXKs0qInmk5DZ91Ga61PA2itTyulXGUeugCbPY5Lc24rcz6vvN11zgnntcqVUrlAJHC28k2VUnNwtApiMplITk5uYPjGGzlzJlkjRnDwwQeNDkUIwxUUFDTr32chLsSOg6UEKPhh83dcDO0c3pLPhcbR2MurVPcvVNeyvbZzqm7U+iXgJQCLxaLHjh3bgBD9xIYNdG7Ths6dOhkdiRCGS05Opln/PgtxAb7I3kW7jAzGjRtndCh+RT4XGkdDZ92mO7tjcf6Z4dyeBnT1OC4GOOXcHlPN9grnKKVMQGuqdhW3PP36gSR5Qghx0cspLKNdmCytIppGQxO9NYBryuhM4GOP7b90zqTtgWPSxVZnN2++UmqEc/zdjErnuK51MY6oNAAAIABJREFUE/C1cxxfy/bll46HEEKIi1q2tVTq3IomU2fXrVLqHWAs0F4plQY8DvwNeFcpdReQCtwMoLXeq5R6F9gHlAP3aa1tzkv9CscM3hDgc+cD4FXgTaXUYRwteb9slHfm7xYtAqXgyiuNjkQIIYSBcgpL6RXVyugwRAtVZ6Kntb61hl3jazj+SeDJaranAAOr2V6MM1G8qLz5JpikAp0QQlzscqyltJEWPdFEJNMwSrduRkcghBDCYHa7JscqY/RE05ESaEb57jtYudLoKIQQQjSRjLxibnlxExn5xTUek19cjs2uZYyeaDKS6Bll+XL43e+MjkKImi1ZAgsXGh2FEM3W0nWH2HYsm6VfHarxGKlzK5qadN0a5W9/gz//2egohKjZkiVw4oRj4pAQwmtxCz+npNzufr1iSyortqQSbArgwKKrKxybLXVuRROTFj2jtG8v6+gJ/3b8OFwEKx0J0dg2PDyOyUmdMQU46gEEKLhqQDQbHqm6IHKO1LkVTUwSPaP8+CMsWwZ2e93HCmGEHTvgbJVKhEKIOkRFWAgPNlFud/xHya7h658y+O7QWSovEytdt6KpSaJnlLVr4f77objmQbpCGMZmg8GDoUMHOH3a6GiEaHbOFpQQbArgyn5RTEnsTGiQid++u5O7X08hPe/85/45q3TdiqYliZ5R5s2DjAwICTE6EiGqN3++48+MjNqPE0JUsfDa/pSU2xkTF8X/3TqI7X+8kj9O6s/GI2e58ulvWL09Da01J7KLUIC1pNzokEULJYmeUcLDHa0lShkdiRBVBQbCs886xuglJhodjRDNzo4T5wBIjGkDQGCA4q7LevDfX4+mb8cIHnxvJ3cu38a6n9LROGboCtEUJNEzysGD8PTTkJVldCRCVJWdDVu2gNVqdCRCNEu70s4RZAogrmN4he2x7cNYOWcEpgDF+gOZnDrn6MZdsSWV2AWfErfw8+ouJ0SDSaJnlD17HOvopaUZHYkQVX3zDYwYAaNHw4oVRkcjRLOz80QuAzpHEGSq+jUbEKD4fsEvuKJfFM6JuVjMAUxJ6lztzFwhLsT/Z++8w5ss2z583umEtpTRlg1l771EtiAoIoqKIijqq+LCgfrh9kXFvfV1oOJERZyogAgoQ/aQXaCMQsto6d4rub8/7oSmbXbTJsB9HkeO5pm50jZPrucav0s7er7isssgMxO6dfO1JRpNZS68EBYtgqwsSEjwtTUazVlFqdHEruNZZ9K2toipE0rDOqFIICTQQFGpiYiQQGIiQmvOUM15gRZM9hUhIeqh0fgjDRvC2LHqodFo3CI+JZeCEiM9m9t39EB15k4Z0JLJ/VvwzaZjnHYwKk2j8RTt6PmK5GQ1Bm3CBGjf3tfWaDTl2b0b8vJgwABfW6KpZlKyC5n+7b/8b3IvHU3yEjssjRhOHL05N/Y983z2lV2r1SbN+YtO3fqKlBR49FHYudPXlmg0lXn5ZZg0CT75BKZO9bU1mmrElXmsGvfYkZRJndBAYhvU9rUpGo2O6PmMzp1VxCRU30Fr/JCnn4bTp2H1ajh82NfWaKoBd+axatxje2IWPZrXRWj5LI0foCN6viIgAGrXBoP+E2j8kHbtVEPGo4/CP//42hpNNWCZx6q7Pr1LfnEpB5JznNbnaTQ1hfYyfEVRETz3HKxb52tLNJrKLFkC+/b52gpNNWKZx2oex6q7Pr3EnhPZGE3SYcetRlOTaEfPV0ip0mNr1vjaEo2mMhMnwscfQ1wcjB4Nmzf72iJNNZCSU3Tm+ciOMZzOLXKwt8YVLI0Y3ZtH+tgSjUaha/R8RUgIlJRAoP4TaPwMKWHtWoiMVP+j2dlQUOBrqzTVwKOXdmTZ3mQAhraPZurAWN8adA6wPTGTpnVr6cioxm/QXoavEEI7eRr/RIjy8203bPCdLZpqJSE178zzgym5PrTk3GFHUiY9dDRP40fo1K0veekl+PlnX1uh0ZQnJQW+/1513WrOaRLS1Czjlg1qE5+sHb2qkpZbRGJ6ga7P0/gV2tHzJR98AEuX+toKjaY827fDtdfCgQNq+YYb4MknfWuTplpISM0jIjSQfrH1OXhaO3pVZefxLMC5ULJGU5Po3KEvSUhQaTKNxp8YPBh27YJWrdRySAgEB/vWJk21kJCWR6uoMNrGhPPD1iSyCkqIrBXka7POWnYkZmIQ0K2pTt1q/Aft6PkS7eRp/JHataGr1TimuXN9Z4umWjmSmkfvFvVoGx0OqDq9Pi3r+diqs5cdiZm0i4kgLER/tWr8B5269SXvv68eGo0/sWEDLFjgays01UxRqZETmQXERoXRrqFy9A7phgyPkVKyIymL7s10NE/jOUKIS4QQ+4UQB4UQjzrYr58QwiiEuMbZObWj50t+/RV++83XVmg05fn8c5g+vWz5s89UhM9o9JlJGu+TmJ6PSUJsg9o0q1eb4ECDrtOrAkkZBaTnFev6PI3HCCECgPeAS4HOwPVCiM529nsZcKnIXzt6vuSPP9QEAo3Gn3jpJVi/vmy5bl3o2BHy831nk8brHElVf8/YqDACDILWUWFaYqUKbDcLJevRZ5oq0B84KKU8LKUsBuYDV9jY717gRyDFlZPqQgKNRlOeunXVw8KECeqhOaewaOi1ahAGQNuYcHYmZfnSpLOaHYmZBAca6NAowtemaM5emgKJVstJwADrHYQQTYEJwEVAP1dO6nFETwjRQQix3eqRLYR4QAgxSwhx3Gr9WKtjHjPnnfcLIcZYre8jhNhl3vaOEOdJl8I338BTT/naCo2mPF9+CatX+9oKTTWTkJZHZK0g6oWpjuq2MeEkZuRTWOLfKfqU7EKunbOelJxCX5tSjh1JmXRtUoegAJ0o09glUAixxeoxrcJ2W76PrLD8FvCIlNLlD6rH/5FSyv1Syp5Syp5AHyAfsKj/vmnZJqVcDGDOM08CugCXAO+b88wAHwDTgHbmxyWe2nVWsWkTLFzoays0mvI8/LC6CbFw8iR06gTz5/vOJo3XSUjLIzYq7Mxy25hwpIRDfl6n9+KSODYnpPPO8nhfm3KGUqOJXcezdH2exhmlUsq+Vo+PKmxPAppbLTcDTlTYpy8wXwiRAFyD8qWudPSi3krdjgQOSSmPOgjGXQHMl1IWAUeEEAeB/mZj60gp1wMIIb4ErgTO/eK1t97ytQUaTWX27VPzbi1ERqpmjPr1fWeTxuskpObTL7ZMSqVtTJnESpcm/tc52uHJJRSVms4sz9t4jHkbjxESaGD/7Et9aBkcSM6lsMSk6/M0VWUz0E4I0Qo4jgqOTbbeQUrZyvJcCPE58LuU8hdHJ/VWjHkS8K3V8nQhxE4hxKdCCMuVxFbuuan5kWRjvcZXSFn+i15zflG/PjRoULZcu7YaiTZ6tO9s0niVwhIjJ7IKykX0WkWFYRD+K7GyZuaISo5UdHgwX93a30cWlbEjSTVi6NFnmqogpSwFpqO6aeOABVLKPUKIO4UQd3p63ipH9IQQwcB44DHzqg+A51B55eeA14H/YD/37EpO2vJa01ApXgIDA1m5cmVVTPc59TduJHrVKvY/9BAEBDg/oAYYNnIkwmRiy0cfkduuna/N0dQwQRkZNFy2jNTBgyls0qTGXjc3N/es/zyfTRzPMSElFKQcY+XKssxQdC3Buj1H6B180ofW2SfJ3EASZIASE6TnFXP9RxsY1SKQK9oGUzvIN+XdS3YXERYER3ZtIuE8KTGvCc7H64K53G1xhXUf2tn3ZlfO6Y3U7aXANillsvmFky0bhBAfA7+bF+3lnpPMzyuur4Q5n/0RQGhoqBw+fLgXzPche/bArl00HjhQRU38gSlT4Jtv6HvRRdCmja+t0dQ0a9fCBx/Q9oorwPrzNXky5ORUm+7jypUrOes/z2cRS/ecgrVbGTukb7m6sm5Ht3AsPY/hw4f50DrbFJUaSV/6B+1iwnl7Ui++2XSM4xn5NIoMZf7mRLakGph5SQeGtYvmvu+287/JvYiJCK0R217avpo+rUIZMcL30cVzCX1d8A7ecPSuxyptK4RoLKW03A5OAHabn/8KfCOEeANogmq62CSlNAohcoQQFwAbganAu16wy/+55x718Ce+/FI9NOcnF14IWVlqvq01/fpBoX91OWo8xyKtEtsgrNz6tjHhrDqQQqnRRKCfdY9uPJyOScJjYzvSuUkdZl9ZNqZvyoCWzPp1D4/8uIv6YcFk5BXzzvJ4Zk/oVu125ReXEp+Sy+jODav9tTQaT6iSoyeEqA1cDNxhtfoVIURPVPo1wbLNnGdeAOwFSoF7rNqD7wI+B2qhmjDO/UYMf6S0tCyFXFxc+ctec+4jBNSpU3n9jBk1b4um2khIy6Ne7SAiaweVW982JpwSo+Roej5tzPNv/YUVccmEBhm4sE1UpW1dm0ay67jSAEzPKwZqrlljz4lsjCapO241fkuVbtmklPlSygZSyiyrdTdKKbtJKbtLKcdbRfeQUj4vpWwjpewgpVxitX6LlLKredt0Kc+TToBNm+DGGyEpyfm+NcG8eUooNyICplWU99GcF/z9N7zyCphMzvfVnLUkpOaXa8SwYN15609IKVmxL4XBbaMIDbJdz7xm5gjG92xCaKD6WgsQcEXPJqx5ZES12rbDPBGju27E0Pgp/hWbP9/IyIB16yA729eWKDp0gKlT4bnnYPx4X1uj8QVLlsAzz4ChwqVhwQJ1A3D0qG/s0niVhLS8MxMxrGkTrdb5m6MXn5JLUkYBF3W0nx6NqRNKREggRUYTAQKMEoICRLXX6W1PzKRp3VpER+gMiMY/0SPQfMmYMXDokK+tKGPgQPXQnL+8/DLMmlV5fdu2cNttEBxc4yZpvEtBsZGTWYU2I3oRoUE0qhPqdxIry+NUj99FHWMc7peaW8SUAS0Z0q4Bd3y1je2J1T/SbUdSptbP0/g12tHTlJGVpcRxpYTMTJXG1VIB5xdC2O4A791bPTRnPUfTzY0YNhw9gHYNwznoZ9Mx/opLoWvTOjSKdBydm3Nj3zPPh7SL4kByDiVGU7WNJUvLLSIxvYAbL2hZLefXaLyBTt36koQEuO46Vavna0pKICpKpe3efluJ5mZm+toqTU3z5pvw3Xe2t0kJRv+eg6pxjqXj1lbqFqBNdDiHUnLxl1Lp9Lxith3LYKSDtK0tbhkUS3J2EUt2n6omy2BnkooY6vo8jT+jHT1fUlwMO3b4h0NVUqLSdqNHK/20N97wGxFnTQ0ydy78/nvl9aWlqht39uyat0ljl5TsQq6ds56UHNelb46k5gMQG2Vbu7NtTDh55vSuP7ByfwomCSM7OU7bVmR4+xhiG9Tm87VHqskyVZ9nENCtqf+NjNNoLOjUrS9p317NFfUHateGBx8sW+7Z03e2aHzH7t22o3aBgXD//bqG0894Z0U8mxPS3dKMS0jNIyo8mIjQIJvbrTtvm9St5TVbPWXFvhSiI0Lo6ub8XYNBcNOFsTzz2152JGZWi/zJpoQ0QgIDyCsuJSxEf51q/BMd0dMokpPLun+lVB3BGRm+tUnjG+xFcmfP1vNu/YQOTy4h9tFFzNt4DCmVZlzso4vo8KRzCdIjaXm0tJO2hTJHL94PGjJKjCZW7z/NyI4xGAzu1wtf06cZYcEBfLEuweu2SSnZmpBBQYmRd5bHe/38Go230I6eLykogAkT4KeffG0JPPoodOyonpeWqqH2b7/tW5s0NUtqqorabdtme7uUkJdXszZpbLJm5gj6tCiLUIUGGVzWjDualldpIoY1DcKCqVs7yC8kVjYfSSenqNRpt609IkKDmNi3Ob/tPOFWetsZHZ5cQqvHFlNsVHWM7jjaGk1Nox09XxIQoORVsqpfAsApt9wCr76qngcFwfvvw2WX+dYmTc2SkgKff66ahGxxyy3QtavtbZoaJcAg2HVCReAFUFRqIiIk0KlmXH5xKcnZRbSyU58HIISgrbkhw9es2JdCcKCBwe0qT8NwlakDW1JilHy7MdFrdq2ZOYJOjSLOLLvjaGs0NY0uKvAlwcGwc6evrVAMHVp++c47fWOHxnd07qxuOux1W15zDfTta3ubxi1SsguZ/u2//G9yL48EfZ/9fS8lpSZa1K9FYnoBE/s043RukdPjEs40YtiP6IGSWFm6J9ltu7zNX/tSuLBNA2oHe/5V1To6nOEdopm38Sh3DW9DcGDV4xvRESEczywAICTQ4LKjrdH4Ah3R06h03I4d5YfWZ2XB/v2+s0njO+xpJ44bB9On16wt5yjWTRTusiIumYXbT/DAqPa8eV1PJDCsfUw5DTl7JKSZNfQcpG5BSayk5xWfmRvrDdztED50OpcjqXmM9DBta83NF8ZyOqeIxbtOOt/ZBfacyCa7sJT+sfX5+e5BTBnQ0iVHW6PxBdrR8zU33ABvveVbG7ZsUV22q1aVrXvySRgwwHc2aWqepUuVI1dQYHu7pUmn2Htf/ucbVWmiAMguLOGJn3fToWEEdw1vQ49mdYkIDWTVgRSXjj+S6lgs2UJ1zLx117n9K069pxFecPSGtoumdXQYn3mpKWPh9uMEBQjm3NiHzk3qMPvKri452hqNL9COnq/JyvJ9gXuXLmqWqXVabupU+OQT+2k8zbnHvn0wf779MWcrVigh7Q0batauc4g1M0cwpktDLDHToADhVm3XS0v2kZJTyMvXdCc40EBggIHBbaNYfSDVJYHjhNQ8oiNCCHciBeJNR89T53bFvmQ6NoqgWT379YSuYjAIbr4wlh2Jmfx7rGpqAkaTZOH2EwxrH0O9MD0SUOP/aEfP1/z2GzzxhG9tiIqCiRNVp62Ffv1UTZYegXb+cP/9qvPWnrxKly5KSDs2tkbNOpcoNUnWHUxDopooSoySpIwCl2q71h9K45uNx7h1cKtys1WHtY/mVHahS3IoCWl5didiWNMksha1ggK84uitmTmC8T2anHFuBTC2WyOHzm1WfgmbEzLcFkl2xFW9mxEREsjnVYzqbTicRkpOERN6NfWOYRpNNaMdPQ2sW6e6f60pLlaNIqmpvrFJ4380bgwzZkCLFr625KwkITWPiR+up6DEyJguDfnlngtpXq8WW49m8N7fBx0eW1Bs5LGfdtKyQW0evLhDuW1D20cDsGr/aec2pOXTsoHzCJnBIGgTE0Z8So7TfZ0RUyeUolIjEjAIkMCWhAwia9kWbAZYFX8ao0kyspN7Y88cER4SyMS+zVm08yTJ2Z5Lrfz873HCQwK96oRqNNWJdvR8zYwZ6uFLbrhB1eRZk5gIPXrYHoelOTeZNUtF7ByRlqZkWDRuEZ+cw7Vz1pNfXMov9wxizo196dG8Hn8/PJwJvZry6tL9vPzHPrvp1zeXHyAhLZ8Xr+pGreDyEdcmdWvRNiac1fGOHb3colJO5xQ5rc+z4E2Jld3HswkwwII7BzKoTQNScoqY+cNOu+/3r7hkGoQF08PLM2SnDmyJUUq+3njMo+MLS4z8sfsUl3RtRGiQHhGpOTvQjp6vMRp9Pyj+hx/gscfKr2veHL7/HkaO9I1Nmppn+3Y1As0R3bsrcW2Ny+w+nsV1H21AAt/dMZCuVnNRAwMMvD6xB5MHtOCDlYeY9eseTKbyzs+OxEw+WXOY6/u34MI2tvXkhrWPZuORdAqK7V9LEsyNGK1cdfRiwjmRVUheUalL+9sjt6iUjPxiru7djL4t6/P17Rfwf2M6sHD7CV77s3Jnf6nRxN/7TzO8QwwBHkzDcERsVBgXdYhh3voEJn64zm0R5RVxKeQWleq0reasQuvo+Zp33vG1BdC7d+V1wcGqRk9z/vDLL873eeUVdROgcYmtR9O5+bPN1AkN4uvbBtiMphkMguev7EpYcAAfrzlCXrGRl6/uToBBUFxq4pEfdxIdEcJjYzvafZ2h7aOZ+88RNhxJY0QH2ylFV6VVLLSNUYLAh07n0r0KkbVFO0+QX2zkun5lKf+7h7chKSOf9/4+RNO6tZk8oGzbtmOZZBWUMKqaUqO3DGrFin0pZCRkuDUjGFTaNiYihAtaN3C+s0bjJ2hH73xn/36Ij4eLL4aQkMrbsrNVY4ZGAzBliq8tOCtIyS5k6qebOJqWR8M6oXx9+wU0rVvL7v5CCB4f24mwkEDeWh5PQYmRxy7tyHVzNnA8s4BPpvalTqj9mrYBreoTEmhg9YHT9h29M9IqrnWxWnfeVsXRm785kXYx4fS2GtkmhOC5K7pyIrOQpxbupnHd0DN2r4hLJihAVGkahj06PLmEolIToGoF5208xryNxwgJNLB/9qUOj83IK2bVgRRuvjDW65FGjaY60albX/PSS3DFFb57/QULYPx42+njhx+G22+veZs0NU92Nlx5JSxb5ni/rCyIi6sZm85iHvlxJ/tO5RAcaGDBHQMdOnkWhBA8MKo9T4ztxKKdJ7nq/XUczywgtkFtRnV23JQQGhTAgNYNWH3Afp3ekdR8GtYJcXnKRMsGtQk0iCp13u4/lcO/xzK5rl9zRIUO/sAAA+9N6U3HRhHc8/U2dh9XoyBX7EthQKsGRDhwbD1lzcwRjO/ZhKAAZUugwXV5m0W7TlJilFzRU6dtNWcX2tHzNcHBUMv5l0C1cd99sGkT1LZxl//sszB3bs3bpKl5cnJU53V2tuP9XnkFunUDk6lm7DrLsGjG/W3ugM0qKKX/CyvcGnZvqVtLyVGTFhLS8l3SnRvaLopDp/NIysi3uT0hLc/ltC1AUICB2KiwKjl6321OJChAcFXvZja3h4cE8unN/ahXO5hbPt/Mol0nOJiSy4BW9T1+TUfE1AklIiSQUpPEIJTcjZTSJXmbhduP0y4mnC5N6lSLbRpNdaEdPV/z4INKpNZXREban1/aqxf06VOz9mh8Q9OmsGsXXH214/2uvRa+/VY7enZYM3ME7RuGn1n2ZNi9JeoUHGBw6xzDOyiZldUHbEsiJaTmudyIYaFtdLjHjl5RqZGf/k1idOdG1HcgLNywTiif3dKPwhIj9379LwAHT1dd1sUeqblFTBnQki9u6U+QQbAmPrVSA0xFEtPz2ZyQwZW9mlaKTGo0/o529M5npIT//c9+Ki4tDRYtgszMmrVL47/06KHEtQPPvvJed2etekJMnVBSslUkztNh95aoU4nJ5NY52kSH0yQy1OY4tOzCEtLyil2WVrHQNiaco+n5FJe679gv25tMZn4J1/Vz3rxz+bv/kFNYiuVVFm4/6dZoOHeYc2NfZl/ZlSHto5k9oSsZ+SV8s8mx3MqvO04AML5HE6/bo9FUN9rR8zVffKEiZ76YH3r8ONx7b/kZt9b8+68aZL9jR83apal5Fi1StZppaY73KypSQtrO9vND3J216gnH0vLJLCihT8u6VRp2b4k6uXMOIQRD20ez7mAaJcbyjtnRVJXOjXVBLNmatjHhGE3yTMeuO3y3OZGmdWsxuK3zpoozUcxA96KYVeXavs0Z3DaKFxfHcTzT9oxnKSU//3ucfrH1aF6/6uPYNJqa5uy7LT/XCA9XchWlpfZnjFYXTZvCqVOVu20t9OsH69dD1641a5em5snNVSLZoU4iT8eOqajel1/CjTfWjG1VxLrTEso6LQMNghUPDaNF/dpn0nEp2YVM//Zf/je5l1tROAt/7j0FwJvX9qJFg9rMvtKzz86cG8vKKdw5x7D20czfnMj2xEz6xZbVuR2xSKt4ENED1XnbvmGEy8clpuezJj6VB0a1w+BCh+qZKKbRvShmVRFC8OJV3Rj95moe/2kXn9/Sr1Jqds+JbA6m5Hr8t9RofI2O6Pmaq6+GX3+13QxR3QgBDRtCXTvSCZGRcMEFyhnVnNtcd52K4IY5cQSaN1cC2yOqN9LiTdbMHMHl3RufWTYI1W1ZapIMe3Ul/Z5fwR1fbeHj1Yd5auHuKkX9/tybTMdGEbRwM3LmLS5sG0WAQVQah2aRVmlZ3z1Hr010OELgdp3e91sSEQIm9nVdc9GTKKY3aF6/NjMv6cCqA6f5advxStsXbj9OUIDgsm6NbRyt0fg/OqJ3PvPDD5CfD1On2t9n2TLVFTx4cM3ZpXFMbq7vnO/QUOcNG35GTJ3QMx2sQQHKwbuuX3OmDoxly9F0tiZk8PO/x1m6J/nMMe7oq1lIyy1iS0I600e0rZb34QqRtYLo1bwuq+NP8/AY80zc3bvJ23mQxpERlcanOaNWcABN69Zyy9EzmiTfb01iaLtol2RlLHgaxfQGNw2MZdHOkzz7+16GtI86E0k0miQLt59gWPsY6jloKNFo/Bkd0fM1y5ZBly5KtLim+fRTeP99x/vMmAGvv14z9micU1gI9erByy9797wPPQT33+/avnv3erVuMyW7kBc2FlStSSIjo6wTOC6uXA1hcamJHYmZNAgL5pd7VLQoNbeIDo0imDKgJW9c15ONj49kTJeGBJrTjCGB7teIrdiXgknC6C6NPH8fXmBo+2h2Hc8i/fAx2LMHBg2i/4KP3ZJWsaZtjHudt6vjT3Myq5BJLjRh+AsGg+Dla7pTUGLkvwv3nFm/4XAaKTlFXNlLN2Fozl60o+dr6tSBTp1808X4+++wxElX2w8/wAcf1Iw9GueUlMBzzynZGzsD4T3CZHJ95vJttynH0Eu8syKe+AxT1ZokbrxRTXeREqZNg+HDz/x+ftyWRGGpidev7UGXJpHMvrJruegRqKhfVHgIRvMxntSILdubTJPIUJ/rrA1tH42UkP70bPV/8vHHPD/kJrfr8yy0jQ7n0OlcjE4kSCx8tymRBmHBjOzkWOTZ32gTHc4Do9qxZPcpFu86CaiRZ+EhgYw6y96LRmNNlbwLIUQCkAMYgVIpZV8hRH3gOyAWSACulVJmmPd/DLjVvP99Usql5vV9gM+BWsBi4H4pvfkt5scMGKCcKV9gMKjokCM62p+vqfEBEREQHa2cmpMnoZGXokdvvun6vm+84bxpwwXsNUm4ky4FlEM3aZKKdgqhotQqYWhQAAAgAElEQVSnToEQFBeXUvfO25g87AqGtR/r8DSWGrHkrAL+3p/CqWzXI4wFxUbWxJ9mUr8WPtdZ69Y0knq1g/hq6LU8c/lIssZN4PC2P5nk4uizirSNCaeo1MRV76/l45v6OnR+T+cUsTwumVsGxZ7poD2bmDakNYt3neTphbtpXr8WP21LYmy3xoQGuZfy1mj8CW98EkdIKXtKKS23yI8CK6SU7YAV5mWEEJ2BSUAX4BLgfSGE5dPzATANaGd+XOIFuzSOiI+HmTPh6FHH++3fDx9+qCJJGt+TlKS6Xp9/HgJ89OVzwQXQs2eVT7P6/4bTs3lZI5DHkhpCwA03qEgjqMkdF18MwB+LN9D94HamtAhy6oBZ9NXuHN6WUhNuRXFWx5+msMTEaCejymqCAINgcLtoFqWAaeK1JKTlMfTwVoYu/saj81k6b3cmZTmNuv78b9KZGsizkcAAA69c3YPM/BKu/2gDJqmceI3mbKY6brmuAL4wP/8CuNJq/XwpZZGU8ghwEOgvhGgM1JFSrjdH8b60OubcJy4O2reHpUtr/nXffls1YzhizRq46y44caJm7NI4ZvJkVTf5+OMqsmcHt8SBi4tViu/rr12z4cQJpbtXBef/ZFYBM3/cxfbEMjHuohIPJDXWrVNalKWllTYVl5p4eV8x987+ns7Tb1Fzeh0JhJvp3aIu7RuG860TEV1r/tyTTGStIPpV0+gut0hN5f8++y8Rxw4TdyqbhLQ8Lj64kTZff2Lz9+SIDk8u4ZoP1wMgUVFXe0LGUkrmb06kb8t6tI1xXYrF35jw/lpKTZLcIuXgrdiXUm3izRpNTVBVR08CfwohtgohppnXNZRSngQw/4wxr28KJFodm2Re19T8vOL684OICPUla0/ipLoYP145eR06ON5v4kQVRWp+dt6hn3PMnKkeRUUqdWsHt8SBCwpUCthViZ/ff1dC2snJzvetgJSSBZsTGf3GajYdSadjowgm929BaAC0igpzX1Lj88/hiSdsOjA/bkvieGYB943phAgKUg7t/fc7rUsVQjCpXwt2JGWx94ST2b9AqdHEin3JXNQxhqAAP0hX7txJsy1rMZhMrD6QypHUPF4dNhXjgQNu1wJbhIytY6Et69fm5au7V5qWsWxvModP53FpN982o1SVNTNHMK57YyzyfzUl3qzRVBeiKqVwQogmUsoTQogYYBlwL/CrlLKu1T4ZUsp6Qoj3gPVSynnm9XNR9XjHgBellKPM64cAM6WUl9t4vWmoFC+BgYF9li1b5rHtGs3ZTM/77gMh2P722+XW3/5nHiU2plUFGeDj0Z4V41ckOD2d0FOnyGnbFulE5Duz0MT7O4q4u2cIRhN8vqeYXalGOtQzcGu3EGJqK8fo3a25HMgSvD2iNgZ3atykJCQ5maIKtYqlJskjqwuIDBE8dUHombRtSHIyRQ2dp1dziyUPrMxnWLNAbuxsJShuMtH1qadIuPlmctu1A2BfupGXNhVyT88Q+jXyD8UqQ3ExT2w2EhYE9UIEBzJMvD7csxq9L/YUsTKxFIMAo4QAVJF1rUDoFRNIn4YBdIsK4Om1BZzKlwxtGsB/ulWv0HF1Y3nPgQYoNcHw5oHc1MWOsLym2sjNzSX8HNJxHTFiRL6U0jsXYjeo0lVJSnnC/DNFCPEz0B9IFkI0llKeNKdlLYMXkwDrsFAz4IR5fTMb62293kfARwChoaFy+PDhVTH//GbGDOjbF6ZMcbyf0Qgff6zqngYNqhnbNLZJTFQaeh06qM5bIaj4GVjbu5AZ321n7SElLxIaZGBMl0Y8cVmnap8yYIsnf95FfOYx5ifUYs+JbEpN8OwVXbhhQMtyExM2nFzO1tNFRLbuQZ+WLqQ/TSYV1axlW6ftm43HSCvcxevX92V4hxib+zhjefq/rNiXwv9uG1KmP7dzJ2zYQNRjj6nOXmD1b3sJDjzK3ROGExbiY0fv+HE18QYYa4zj03+OENugNh2bhTCcQnj0UVi8GOq7nmL+NnELUy4IZXL/Fnyz6Rinsgq4vn8Lluw+xbK9yRzbe5R3IxuCEETlZbD6eD1WH89zv6nGj6j4nk/nFDJ8eF/nB2q8ysqVKytd4zTu43GeQQgRJoSIsDwHRgO7gV+Bm8y73QQsND//FZgkhAgRQrRCNV1sMqd3c4QQFwh12z3V6phzn/x8iI11rmfnTaSEv/5SGlvOMBiUlMaPP1a/XRrHfPKJGkdXWAhXXKHS7xWIqRNKXnFZGtOlurffflPNFc4acywUF6tj9u2zu0uHJ5cQ++gi5m08hpSw4Ug6OUWlGE2SqQNjK43F6hYVQKBBsDwuxc4ZK/DTT9CmDRw4UNm8UhPv/X2Qns3rMqx9hTrG4mK47z74xnljwqT+LcgpLGXRLqsUeffuyuEeMQJ270Z+9x1/7j3FkLZRvnfysrPV/8fTTwMwrF00JUZJfEqu0tALD1cOspv1tpYmlc5N6jD7yq58clM/RnZqyGsTe7B1TCSr597FrcfWMyBxN/98eCsXH9161qc6K77ninI8Gs3ZRFUKShoC/wghdgCbgEVSyj+Al4CLhRDxwMXmZaSUe4AFwF7gD+AeKaWlneku4BNUg8Yh4Pypeg0JgWHDoGXLmntNIZTg7QsvuLbv4cPwyivVb5fGMVOnwvffq1o6o1E5OadPV9rteEYB4SEBNKoTSkRoIMk5TurewsLU/5+rKRIplZPpQBbIUtsVFKAcukCD4IoeTfjnUdtf/mFBgn6x9Vm+18W6v+bNYfRo5exV4IetqjbvgVHtKnfaBgerBiMbDmJFBrSqT+uoMOZXbMpo0kT9zp55hpKH/o/TKZmM7uJmt63RCJmZzvdzh6AgmDULrroKgD6x9Qg1S5xEh4eoCP6mTd6ZXW1u4grs1xfDE49jGDOa7U3as6DXJWyLaVsjc2o1Go1reOzoSSkPSyl7mB9dpJTPm9enSSlHSinbmX+mWx3zvJSyjZSyg5RyidX6LVLKruZt088bDT1QEhlffAGXXeZrS+zTsKFvBJ015WnT5syXOCdPqhRuBWfLZJKUmCRjuzXm+QldyS4sZYCzTtCLLoKFC6FBA9fsCAmBLVtUN7YdyobUq4+yUUoiQh1/+Y/q3JD4lFyOpuU5t2HAANWIUUFixmE0z8LWrcohcoIQgkn9m7PlaAbxyTmwcaNyto+b56F++ilfvfwFxcEh7osDP/mkSp+eOqWWi7ww17VWLdVsYpa+CQkMoEG4qqG07m6mpEQ14HjKG2+oUo7cXPX7nzWLYwFhTBzUjr6/fMWlI7pzOqfQ7Q5fl/jkEzh0SD3ftMm7ouEazTmKH7SIaWqcr79WMh3Fxa7t//ff3h+5pXGPnByVLs3IUMtNm6obhEvKS04eTs0jM7+Evi3rM7JTQy7qGMNby+NJcUP81yX69HHqGKbmFBEcILioY4xLQ+pHdVK1dA7TtyUlytHItt0N6zCaZ8Fgvuy54CRc3bsZQQGCbzclKgdj+XLVKQ8QEcGPqYH0bVmPqE8+gH//dXyywsKy0WwzZsAzz5QJXt99t9In9NRx+fZb9f9hxpI6P56p/u4rD5wm9tFFDJzxLTRurBwmd7HYNmCAiqZa2Vou1TmuI3N+eRH+7/88ey/2yMyERx5Rf/9Vq5Qd8+Z59zU0mnMQ7ej5A716wYMP1tzrnT6thJCddEyeYcUK+O9/y2aJ1hQrV8I999Tsa/orW7aodOnmzWpZCBVdatWq3G5bj6oAeu+WauLJ0+M6U1xq4qUl9uvpuPNOuLxSk7tj1q5VUUAHTL+oHcVGyeU9GrtU59SyQRjtYsJZEecgfbtihaoZ/eefSpuSMvKZ9etuujSpYz+aZ+GGG+DWWx3vAzQID2F050b89G8ShROvU1JDdeqceb29J7MZ26K2cj7mzrV/IqNRNTP95z9qOSYGnnqqbPuwYXDllervCnD99fDaa07tA5TD9e676mHGkjoPDVKXeItEyMJnJ8Att7gneC2l+hxaSj0GDVJjESPsaOUFBkLr1tCiheuv4Qp168L27eqmc8gQZcO113r3NZyRkKBssDB+PLz1Vs3aoNG4iXb0/IFLLlHOXk3xwAMqfeUqTzwBeXllkZCaYudO+PJLm3Vo5x0XXKCcq4EDy9alpMCff5bbbUtCBvVqB9EmWnXwx0aFcfvQVvz073E2J6Rjkw4d3K/b+t//4OGHHe6yOl793Qa1jXL5tCM7NWTTkXSyCuyIMV9yCezaBZdW7uZ8aMEOio2SmIgQ52PIWrVSTVAucH3/FmTlFbF0z6lyn4Fl5nrC4f3bwfr1SoC8IpaoeUAATJ9u/8Zl6lTVEQvKKSwuLps9LCV89539dKsQKsL15ZdnVllS50WlJkICDeVn9776qnKUXKW4WKVps7JcP+a111TU0lts2KB+Nm+uakkNBnWDEhKi6gWvvda15jJ3+fFH5cRbuPnm8iULjRurz6GFPBfKDjSaGqZKOnq+JDQ0VBYWejkdpfEvDh9WUwxGjvTKbNVzjtdeU+mx1NQzadSLXltJ6+gwPrmp35nd8otLGfn6KurWDub3ewcTYHBDp84eSUnqy7ZJE7u7XP/RBjILSlhyv3OnwiKjsPVoOld/sJ53ru/F+B4Vzl1SohoOKlBxZq4Fb8l7mEySH4deTafUY3SN23wm6nb9RxtIyyvizxnDynZOT1cRx9dfV/V3Y8fCp5+qOkhPWb1aRfy+/BJuvLH8tpIS9XewMQ7vjq+2EB1RXiLkTFT11CnVfdu7t2s2WL4n3J3ju3atisAtWOD5Z3jxYlXD/NNPMGFC5e1xcTBqlBrV6G5k2hm3365+//v3q+V165Rz2adP2T4mk/obbNqkRu/9/rt7jrTGLueavIoQwic6ejqid76Rnq6iQ3/84foxaWkqqrdlS/XZVZG331YRo9GjtZMHqh7POmUEKoqxevWZFFp6XjGHU/Mq6dDVDg7kycs6E3cym282uiih4oxmzRw6efnFpWw5ms7Qdq5H8wB6Nq9H/bDgyulbi37gZ59VOmbNzBF0bxp5ZtnlSQZSlo/G2MFgEET368k/dWM5kqa6TTPyitmUkM7ozhWmQMTFKYdk2zYVMezWTXXoVoUhQ1Sd7MSJavmrr1SaNzNTaVx26WIz6u1QIuSqq8rSyPY4dUrtl5SkHDx3nTxQ1464uKqNUBw92rET16mTmt1t2b5vn+e1jlKqG6hNm9Tyu++WlxG68MLyTh6URXnr1lWOqCUtvm+fzkZo/ALt6PkDl19ec123WVnqi8fV+jxQd6wvv1zZ0ahOpFSve/Kk6q48nykuVpGF774rv75FC+UEmP+WW4+qRo0+5vo8a8Z2a8SFbRrw6tL9pFk3RUipGjtef909m44dU1++qak2N288nE6JUTLYTUcvwKCaN/7el0KJ0SpKl5+vvmQ7dqx0TJ1aQcSn5AJUTlM64rbbXG6A6PzMTF696D/M36ykVv7al4LRJCvLqgwapOq4Ro1SMji//aaaBqqCEEqc2XLDk5+vnLzISJWCHjwYotz7PfP6685nG+/dqyJylgYgTxg/XqVUW7d2/1iTSb3XwEC44w7Hnf+W8X2HDqko5UsveWZvTo6q/fvCPK49NNR1B7d9e3WtstQuTpsGQ4fqzmCNz9GOnj8wZkyl7kmPcXZRadVKFbS7k0qKilLyD7fdVjXb3OGBB+CXX+DXX1Xx+JEjNffa/kZwsIqqPPBA5W1r1qioHrDlaDpBAYLuzSIr7SaE4JnxXcgvNvLan/vLNpSUqC/j9u3dsykuTtUq7d9vc/Pq+NOEBBroF+v6BAYLozrFkF1YypYEs4NRWqqaF+bNK1+jaGb+pmMUlBgZ1SmGn+8e5FKHL6Aioo89VlYLZ4+DB4kJC2Jkxxh+3JpEcamJZXuTaVQnlG5NK/+uqVfZ0fYqd9yhGpWEULWKn3zifrRt4EAVCXTERRepz123bh6bCqj/X6MRZs+2+/9ikzfeUE6bnZsJm7RuDc8/XxatTE9X/+POKCxU1846dVR61qqxxWM++kidRwjltH7wgYpMazQ1jHb0/IHp0+Hee6t+nsOHoXPnssJlbyGEzRqgasMsxgrApEkqLeNi4fw5S0yM0jOsyIMPqnFowNaEDLo2jSQ0yPbfql3DCG6+MJb5mxPZmWTWVQsOVl9A7tY2DR2q0nEXXGBz8z/xqfRvVd+uLY4Y0i6a4AADy+OSVSfr4MF25VQKio28t/IQA1rV5+Opfd2bZDBmjIqUOooUGY3q9W+7jev7tyA1t5jfd55g1YHTjO7S0HnThz+zezc8+2zlm8PFi1X6GcoiZVXl9GlVjjF/vuvH9O6tIqOu6juCulbNmFH2WbnzTvU/6kgxIC9PTTqxdEE3bOidxrOOHZX9oCKjd9/ttFNdY0ZK2LiRAOvvAo3HaEfvXCIzU4mwWmQNkpPVnao1t9yiOsfcZe5c1yZpeINrrilLZUdFQdu2ntUHnSv88ENZKqkiX3wBX31FUamRncez6GsjbWvN/aPa0SAshMd+2sW1H64jJcfDhqZatVTHoY0bgJNZBcSn5DK0nROJEzuEhQQysE0DlsclI6OjHQp2f73xKKdzinjw4vaeOV25uSrCbQ+LdMlttzG0fTRNIkOZ9dseCkqM9Iut5shddbN+vfpMJySUX//OOyoq5izS6Q6NGqnSD/N4Nr74QjlkjrQ8L7pIdXdX5bM/dapKoVoct4rvFZQz269f5do7bzJkiFI6mDRJLf/yi2quqWnJqrOFzEy44AJaz5nja0tqHCHEJUKI/UKIg0KIR21snyKE2Gl+rBNC9HB2Tu3o+QP33ONZDUtFevdWd46WIvl771WyLdYXk+bN1cNd1qyBJTU0mW78+PJzXLdtU9IT5+tF8bPPVNTNFp07Q6NG7D6eTXGpyWZ9njURoUE8PrYje05kszkhgz9e/lTdHOzY4b5dc+bYdJLWxKtU25D2btaNWZCSq2plczQtn0MXjFBfijYiS3lFpXyw8hCD20YxoLUbUR9r3n5bRV1OnrS9PTBQNUEMGkSAQXBtv+ZkF6iJD+sOpXn2mv7C5MnqZrCCFiMLF6rOUW9H8Zs2LXPa9u9XmQdLrfD27WU3pY8+qpxNbzBunEp1g7o2tmmj/p9ANZedOKFseucd2x293qR377Lf6Vdfee89nivk5anfC6jyh//9j0MOpu+ciwghAoD3gEuBzsD1QojOFXY7AgyTUnYHngM+cnZePdfKH7joIhUd8TZ33aUG1VvuZr/+WnWymgVf3aImGyLuvLP88u7d6gt52jTvOMRnG7//bn8uano6/PgjByLaAlTquK2ItRSJBOYfN2JqNYiPPtvDurec3hiWZ9Ys9UU6cmS51WviU4mOCKFDQzuCus549VXG//e/vHXj2yyPS6FtjO3zfLn+KGl5xcy42M36QmumTlWp2Wgb0UejUUWerrgCGjSoJOPy7aZEvt2U6DUZlxqnYjfw11+rLltLtLY6eeGFshFpRUVKlmTMGHWd2bu3aiPa7NGlixo9d/HFqjxk3DhVq/jzz95/LWf88INKZxsMypaHHlLRy5oskfE35s5VI/y6d4cePeCeezCtXFn2f3J+jOHsDxyUUh4GEELMB64A9lp2kFKus9p/A9DM2Ul1RM8fuPpqdQHyNiNGlKVp4+LUNABPRh/VJNZ39hYmTlSdf+ejkwcq4mCvwD8zE6ZNo2T5X7RsUJvoiBCHp7JMTAgOUB/9+CZt+Pf/nuWX565y367du1XnrRUmk2TtwVSGtI3yvH7tP/9BvPACtTp3ZPle21MycgpLmLP6EMM7RDuNYjqkeXOlUWfrS2T9ejU94++/gcq/O5dlXPyZI0dUmcScOer6UJOpMsvvPChIjXCbMUOtW7jQ9akg7lC3rho7FxamIsTff1/p/7fGEELV3YKKNH78sZqlfL4RF1cm23Xnneoz16PshjMwK0tFQn31d6p5mgKJVstJ5nX2uBVwmmrTjp6/IGX1tuF36qTGZ02b5tnxe/fCTTfBgQPetcua4mIlI3HffeXX16p1/mrprVunorBpdtKEsbHIQ4d4t8Vg+rRw7vBYJiaUmNPgJa5KkdiiQYNKEYi9J7NJzysuS9taD7Zfu1al4W1gKCxUo6RMJlWXOWMGo7o0YtuxjPJyMGY+X5tAZn4JD1YlmmchMRFefLHyVINBg9T82rFjgfK/O7dkXPyZqCglSdKwoerenj695m0wGFT63FIjJ4RNYWyvM2SI7Qanmubii5UEzYUX+tqSmsVkUulyi5pAcHCl5q7SOnVU/WTLlj4wsFoIFEJssXpU/EK2dXds0zEQQoxAOXqPOHtR7ej5A6++qi52RS5IQlSFvn3V+CBPyM9Xkg4uCMx6jMGgtOLuvrvytr/+Uik0R8Xb5yK7dqn6PHuOrsHA0ToNOZ1fSh8XmwNSc4uYMqAl47o35pU/3uHue8Y7P8gWy5crfUUryo09++MPlS6ziOU+8kh5iZjHH1fpKiBm5UqVvrLqGB/VKQaThL/3lxedzSoo4eM1hxnVqSHdm9X1zHZr4uOVLRW71YVQ4rdW9YGW351bMi7+TESEiqpceaVyfM6P9Jj/0aGD+rl2rfrMn8tYAhoGA3zzjePUuRAqpevtiSe+o1RK2dfqUbG+LgmwLqJvBlRSGxdCdAc+Aa6QUjotFtafan9g4MCybjR/pW9fVe9XnQQGqjodW2Rnqy/kpKTzK4V7xx1Kv9BB7c6RhUu5Z90v9H1gqEuntEiPbDqSzhexvekyvC/2Z1w4YPlyVVA+c+aZIvs1B1Lp1LiOinI1baoeFr76SgnSWtiyRRXHA6fGjKHjpEllUwWArk0iaVgnhBVxyVzTp6wMZe4/R8guLGXGxe08sboyQ4ao/ytrW9etU0X7jz6qmlXMWMu2zL7SzfnA/sr53NHuTxQXq/R5hw7uTS4627jnHnXj+vrrro/gMxqVw3fBBaqG79xlM9BOCNEKOA5MAiZb7yCEaAH8BNwopXQpxaYjev7A4MGqduR8TU+Cust7/331hWuLK65Q6ePzycmz4KRAu+jvVdy/bj7twtz7wu7bsh5bB1zMm709qM8D1YyRm3vGUcgvLmXXwZPcmmqeoNKtm4rEWrrAW7Uqf5H+88+ybmJL9MwKg0FwUceGrD5wmqJSJfWRkVfMp/8c4dKujejSxIZYsScEBZV38kBJYXzyiSob0GhqguBgJRDvjtbg2YbJpG7oAwLcu8HIzlZR93N8SpKUshSYDiwF4oAFUso9Qog7hRCWLsWngQbA+0KI7UIIp7NJtaOncZ2HHqqUqvMau3erOz17mmbnY9QhK0ul1Nascbjb+10v5c73V2KIcC8tbzAIxndqwOoDp8kqcGF6QEVCQ8sJy248ks4t63/k6hce8Fot58WdY8grNrLhcDoAH685TF5xKQ+M8kJtnjVJSUpjcvNmtXzvvSrlrB09TU3SrZtqGjEaz82onsGgsgCvvOLecfXqqfnD7o5qPAuRUi6WUraXUraRUj5vXvehlPJD8/PbpJT1pJQ9zQ+n6vDa0dO4TkJC1YaTO6JbN1UU7kjL6uefoV07u1MSzjlOnlR6Yw7U4bPyS9iZaaRXG8/EiWfePIyZf85hmZ3uVoccP67usvfuBSlZcyCVzwdNpGTxEvdHqtnhwjZRhAYZWL43mbTcIj5fl8C47k3o0MhD6RZ7RESoiRDx8WXrzucIu8a3fPSRGm93rnTi5uaWTTkCz27cW7dWx2VnV4/8zjmMrtHTuM6PP1bv+Z2lZaOjoWtXJbXiiRbg2UbHjqpQ3gHbjql5sOOXfAEnO6oaH1eRkoBHHmHPoUAydp4oVwfnEjk5qpHo8GFISWHDmEfp2a4RwWMGuHceB4QGBTCkXTR/7jnFsr2nKCg28sAoL9XmWRMZqRxrg0FpeWVm2p9GotFUN7fequRX+vd3vF9amhK97mzW1P3uO9W44K3Rdd5i/35VxnH77epm3VNSU9V3wN13+39dux+hI3oa33PkiBJ3PnLE8X6DB6uo3rnTal9lth7NIMAgaL78d9UV7Q5CIB5/nJhrLuef+FQy8tzsaO7QQXWKX3cdRUUlHD+R7vHYM0eM6hRDck4Rp7KLiI0Ko020h53jzrCkoevVK9eAodHUOMHBSl9VCDh2TDk4FqyVBy6+uGzyx+7dKmpmb4qOL+nTR90QVhBXd5uoKOXkmSWPNK6hHT2N6yxZoqZ42JvS4Cm7dpWNvnGF82XQ9XXXOa2J3HI0nc6N62CwNA+4Q3Ex5OZyebfGlJokf+w55d7xQijnaMIEfn1zHlm1IhjczsOxZ3bo8OQSHvmxTG7iSGoesY8uosOT1TCOr6BA3UxERsKbb3r//BqNuxQWqv/J229Xy7Nnq7IIyzjI115TU4NARbpWry6Tp/r3X6cZgWpn27ayyLi70l45p+j57+OQU6Gs5OmnlQqEN0hOVkLN33/vnfP5KdrR07iOyaQiONYSGd5g/HiVgqg4c9MW8+apYuVTbjolZxtSOp3tW2I0sT0xU02G8GR00tq1EBFBlwPbiG1Qm993el5/ueZQOlHhIXT0cu2cZRpFgLmkp1qnUdSqpaLFDTycm6vReJvQUCUk/uKLarl3b3UDaLnZveii8hIlQ4aUNRA9+KAa82Y01qzN1rz7rpr6lJvr/rGrXiEyay+ssnGzm58Pjz2mHFtPsFxbQ0OVjFKU+QZ11y41XKCipuZZjq7R07jOZZepR3UQ4nh01xl69VIXsOqcIlIdWCafGFy8txLC6V1m3MlsCktM9I2tp7pcn3tOXfw6V5yBbYdWreDllxGdOjGuJJv3Vx7kdE6R0zFqFTGZJP8cTGV4+2jPx57ZwTKNwgQ1M43i66+r57wajadcZSV/NHas62nLBQtUOUxAgHJsfvtN1e+5eg3yBh9/rCbPuBPNmx0DpUqIXABsmasegSHwpDRBTQAAACAASURBVJVg/7ffqlrEoQ70Q3NOwQ+3wDWfQ4R5CsrTT6sml6VLVfT+2DGVKgeV5YiNLZOE+vln5WT/+KMal3iWoiN6Gt/y1VeqbiM93bX9u3SBl16q/qHr3iQrS80dtsxrTEz0iqO6JUE1YvRpWU9dyFeuVJ2wrhIbq8SOGzdmXI/GmCT8sfuk23ZUGnvmZc65aRQaTU0QHV3WzPHzz0qqafFitbxli7oxtJTh5OR4N1Pz++/qfIGBrmVqrLlvB0R3KFsODIVuE+F+q4khtWvDzp3w1FNq+a67lPxXRVa9Asc2wML/ll1zGzdWjX+WWkeLkweqlnDJEmjRomxb3brQqJFa/vRTdc30ZZTUA7Sjp3EdKdVMyvfe8945LXVe9dwYTC+lmg3p71G9ErM2XZ06qoMuPFzZ3bGjutN1xFNPqbm/Dt7j1qMZNK1bi8aRtdQ5ExNVcbarZGYqJxTo0DCCtjHh/LbTfUdvTbwqFB/UtnocvTk39mX2lV3p3KQOs6/sWm46hUajcYEJE1SK0pKR2bixfNfqnDnqOmW+HvDbb2pcoeUalp5eOf1qfW1as0aJPYOqK5w507OuWClh00dwer9aBCgthODwsoicBWvlBYsIs4UWQTAsREUCk0rgljkwOUxFC++6SzWsWDt49rjsMiXsbpm9vHevKnmxvNbPP8OOHe6/zxpGO3oa1xFC3Ul5cx7mDTfAsmXu6SrNnasKjw8e9J4d3ubrr5WMQGamem8LFsDUqar+48EHnadfmjVTKVg7vxcpJVuOpqtonqc8/bSK6gFCCMZ1b8zmhHSSswvdOs2a+NN0bBRRfelUjUZTNQwGNV3Icj255x7VfBRpni4zYoSSSrI4T7t2qWuW5Vr/7LMqqmVx7u6+u3xt4FtvqXGBoOre7rhDTc5xBynhr9nwzxtQtwX0vZUjsVPUtkN/Ob6xf/fdMhFmkwlGT4GOPQEBjQzQvxYMGVc+KugJr71WVhdoMqnfo3XD3EcfqUijBT+J/GlHT+Mev/5a1s5fVXJyPIvKXXKJCqFHe1/Ko0qYTOpuFlSKuX//smULBoNKmTRrVnaMFSnZhVw7Zz0pk29SI+HscDyzgOTsIlWfZ2HOHJg40XV7J05UFy4z47o3QUpY5EZUL7+4lC0JGQxt72d/C41G45jQ0DLHr08fePjhsuXHH1dlIJblq6+GN94oWx44UDmOFt56S+nkWbj//jIn0hWkhL+fhzWvQe+bVPp23Bsci70WBj0AmUdhk5MsiAWDAV59BlomARICDHBxEETXrhwV9ARLNM9gUA7xCy+o5dxc9d24xKwIkJOjas8tGbC8vKq/todoR0/jO+69VzVXuOvsNWumxlXVreva/tXVobt4sYpGWhgypKxmpGdPdUdsqe2oSGkpTJmiZhxb8c6KeLYcSeWdZY5HiG09qurzerewcvTy81X3sqt3kUOGKGFWM21jwunUuI5b3bdL95yi2GiiW9PzQMBao/EVOafgs0srS41UJ9bZhCFDYNq0suUbbywfsWve3P61zhVWvgirX4XeU2HcW+UbRkY+De0vhT8eVZE9Z6TGw6eXqJRvp/Fwww8QVBv2L4FsL092atDgTFaEsDA1OcoihWMyqSinJfKZkmLzFDWBdvQ07vHOO+ruzxtcdply2Dzp1MzIUJ1Qzpya//s/VeuWmOiRieW46qryd7GzZpWfvdivn+u/m8BAlQY3dxt3eHIJsY8uYt7GYww/uIWHpwxizK3v2dWL25KQQVhwQHk5kxkz1F21q1IrR49WGic3rntjth3L5HimayOG5qw6DMDag2muvaZGo3GfFc+ppgJbUiNnO3+/qN5Xrxth3NuVu4INAXD1xxDdERbcrBw5e5zarRxiYzFMWwnXfQVtR8F/loIhEL6+Foq8LA9mQQjV6GERW4+MVLqHAweq5ahahATgk/oWjx09IURzIcTfQog4IcQeIcT95vWzhBDHhRDbzY+xVsc8JoQ4KITYL4QYY7W+jxBil3nbO8LbGg0a7xEVpWrPSkurfq6JE1WI3xMWL4ZrrlGioI4YMAAmTy5LlVaFwYNh2LCy5e+/Vy3+Ft56SynTu8pHH6kUCWa9uB5NMAhICa/P4o6DadG7k129uC1HM+jVoh6BAVW4Vxs0SDmHVlzeXckKLHIS1bM4pvtOqYvm/M2J1SdkrNGcr8yOgVmRsH0eSJNqLpgVqdafzVgilEufhFUvQc8b4PJ37Eu/hETA9d9CQBB8cx0UZFTeJ2krfH4ZGILgP39Ao25l2xp3h2u/gJS9sOAmMJZUz/tyxKpXCDD4JrhWlRctBR6SUnYCLgDuEUJYBLzelFL2ND8WA5i3TQK6AJcA7wshLKGHD4BpQDvz45Iq2KWpTiZPhvnzq96QsX59pWiSW4wdq7qfevQov15KVatm0aC75hrVdFBS4rlK/ObN6rwPPqgeFlq2dK9buCKW+5lNm4h582Vyi0oxSdjbuC2Pj5nOssQC/rtwD0fTytd25BSWsP9UduVGDClV162lKNkZr7+uIqpWtGhQm+7NIvlth+M6va9u7U9krbL/gWoVMtZozlfu2wG1repfRUBlqZGzkVWvwNH1sP5d6DkFxr/rXN+vXku4bh5kHoPvby7vrCWshS+vgNBI+M8SiLIxT7ftKBj3JhxaAYtqUIvV4qxvmVszr2cDjx09KeVJKeU28/McIA5o6uCQK4D5UsoiKeUR4CDQXwjRGKgjpVwvpZTAl8CVntqlOQsoLlZp23vv9fwc9erBhReWtb1bMBrVyJ2KYsOTJ8OYMeXnRLrCvn0qKvjWW57b6oxffkHOnUv8vmPUDg5g4ZQuTOrXnDbR4aw6cJpRb6zi2d/2kpmvbF+5PwWThDbRYeXPI4SKuLo60Py661SUsgLjujdm1/EsElIrFw9LKVmwOZGbPt1MQbERQQ0JGWs05yMHl0P+aUCo1KM0qvRk+Fka0Svn9Jgdre1fwwsu1ve1HAiXvw2HV8Kv96uo4K7vYd7VUKexiuTVi7V/fJ+bYOj/wbYvVeNHdSMljPyvijL6EK+EEYUQsUAvYKN51XQhxE4hxKdCCEvYoSlgXSiVZF7X1Py84nqNP3LihErdfvON5+cICoJFi1SXV1U4fBief151tm7apLqaAgPVuefPL7/vww8r7bqKjqEz2rWDzz5T0ijVxaxZrP3pLxIJ5b/Dm9Ote2teOrqcFQ8NY+XDw7mmTzM+X3eEoa/8zSdrDvPhSlUX98/B1Mrn+vZbmD7d+WsWFChNKBtzgy+zpG93lY/qZRWUMP3bf5n54056Nq/LwDZRTLlACxlrNNVCbgr8+STUqg99/wO3/w0Nu8HpOPjrOV9b5z6lRTB4hkq/Wgis5X6EstcUGDgddnwNR9fBj9Mgqi3cvBjqNHF+/IgnoPskJeWy47vqa3TJOApfTYClj0Pt+pjnfPgEIasYvhRChAOrgOellD8JIRoCqSh3/TmgsZTyP0KI94D1Usp55uPmAouBY8CLUspR5vVDgJlSysttvNY0VIqXwMDAPsusOx41NYKhqIgOr7zCqbFjyfBWU4aHNFi/nm6PP86eWbPoNHs2SRMncti6M+wsQUrJcxsKyS408kXBakKzs8no3Zvc9u3P7JOYY+LptQXY+rQGGeDj0WE2ttgnIi6OPnffza4XXiDNUixsxewNBeSXSMKDBXf3DOF0vuTDHUVkFEmuahvE2NZBGLxYSpubm0u4u0PPNZpzmM57XiUqdQNb+r5Nfpi5xlhK2h/4gCYnl3Ko9VQSW1ztWyNdQZqISVlNqyNfU6swhcLgBoQUp2MyBGIwlXKi8RjiO9xl81Bb14Uhq68hwFS5xs5oCGLN0B9cMkmYSui+8xkis+JIr9+LBmlbHNrhFtJI0+OLaX14HlLA4dY3US9jB8XB9Wh+x3eFJilrVf1F3KNKjp4QIgj4HVgqpXzDxvZY4HcpZVchxGMAUsoXzduWArOABOBvKWVH8/rrgeFSSodibaGhobKwokaZxv/JylJ6TLffXvUGicJCpVUUHa1StaNHO9ZuMhrVa0dFVapNs8ndd6uh4ddcUzU7nfD3vhRu+Xwz8+omMvixu5Qq/bhxlfZLyS7k/vn/suFIOlKqurgxXRrxxGWdylKmK1fCTTepEUTdulU6xxlSU2H5ctVcYmOc3Kf/HOHZ3/cigG7NItlzIpsmdUN5e1Kv8pIuXmLlypUMHz7c6+fVaM5KDiyFb65V0adhM8tvMxnhp2mw+wcY+xr0v71mbbM1P9be9pQ9sOy/cGqnao64+FnYPBfCG0LfW2DLZ5CbDJNsz5i2eV3IOaWaOPb9piRUAkOh0+Uw+nn3dPKeiwGjjSxExZm6rmB5zyOehBXPQOJGc03gW1C3bEauECJfSuneXbkX8Lii3twZOxeIs3byhBCNpZSWnM8EYLf5+a/AN0KIN4AmqKaLTVJKoxAiRwhxASr1OxV411O7NDVESooa6+Uu69aplvNLL626oxcaqh7gmlCwwaDSurGxzh29nBzYsEE1XFQjUkreWHaA5vVrMeDB26Frs7IxRRWIqRNK6+hwNhxJt18X16SJ0rxyJrFSr57dDuEOTy6hqFQJOUtgZ5Iai5ScXVQtTp5Go7GiKBcWPaTkRAY9UHm7IQAmfAjFebD4YQipAz2uqzn7LPNjV70M4yrFd8oaLT4aDjknILIFXPUxdL1GXYPbXFS2r63jnRHRSHXhGouVk2csVr8Dd8WQH9gJv8+A/eb5vxigaW+46iP3bfrrOfWePx8HtSJhwhzofp1n0mHVQFVaJwcBNwK7hBDbzeseB64XQvREfUckAHcASCn3CCEW/H97dx4eVXU+cPz7zmSDhH1fRRGRVVBWFQXcRRFcKIoLClqtVqs/6wZaW6lV26qlte4oIqiooIi4AIosUvYlIDuCshNQCIGQZOb8/jg3MElmkkkyS3J5P88zTzL3nrn33DB3eOcs7wF+wM7YvdsYk58E7S7gbaAK8IXzUBXV+PE2UPrhBzj11NK99rLLbMb1BhHIUF5aIjbQSw3jC1W1anbhbydP356D2dzz3jL+c0PniE44mLFmD+nbD/D3azuSmOAN2pIXKOPQUYZ0P4kbujVnwsKf2JtZqFX7tNPg3XeLP+lLL8G4cTB3btDZ03Me6sOoaWuYumIHfgOJXuHyDo0Y0a9NaS9PKVVa34yCA9ts7reEEOuxehPhurdhwnXwyV022FkxIXQrG5TcEleSUfXtOLt8i9+0D/FAsx7w03wIHFyS6aRoytoNHQeV/nzFydoDZ91asFWwtKo1hLSG2IkuXvDnwfbF8Mq50G4gdL4Rmve0/28U/tvlHLatdu9eYyfIHGNs6pfP7oUzSpFqK8rKHOgZY+YSfHThtCDb8l/zV+CvQbYvBtqXtS4qxvr2tWlGSrPETaDyZFAvr/wgb/9+2xoYbIbqli22jikpx6b8j565gUVb9jN6xgZGDSymS7QUjDG8MH09LepUZWDn8OYfvXpTl2O/jxpQzC3j84Vu1WvUyLZUZmUF/TesXz2FaskJGCApwUOuT2fUKlUmpQ2uti2BBa9A12HQvHvxZRNTYPB7MG4ATLkXMEVb2Yyx+ff8Pvj26eJb4kIxxgY1LS90Wr8CgrnEVNs1KR5o2Qf2bYKD2+z5ArtUIy2wq7csrYL5svbYiS75AePedVCnJayaZGcD125pJ3/sWWtb7N4bbFfZ2LbQBtfihap1IftXGygmVIE2V0Tnmsuh3JMx4kXH6FVCL78Mc+bA229DUohvqrGwfTu0aWPz6z34YMHWurRk6NHDtnTNm1egGzNQcoKHdaMuK1c1vly1izvfXcLzg87g6jMjkNA531132da6dGcm25Ejdibu2WcXWPKsOL8dt5h61VIKtBwGBpmRpmP0lCtNfQCWvGVbn0oKSHy5trvz8H64ewGkhLGsYOFWtkDisUFeKN4keGwneAPaewID0+Q0m7pk0RuwK912j9ZoCnvW2JZGX27R6/rsflj6tj22Lye86y5G3D4XcrLghynw6V3B8+2JF26YCM17wNePh33NlW6MnlIsWgTr19s1W8ORmWlb0uIZ5AE0aQIPP2y7kSnYWven/u3Y9n8j2bItgy8/WkmzWlXYuPd4Prkkr4fLOjQsdzem3294ccZ6TqmXSv8zwkgJUBrnnWfH6uVLSbHLnZ1yStiHCLvlUClVVKhuzuIG+n//b9i9CgZPCC/IA7hvZcGJCZ4EqH0KnNLbBmYeL+Qcgc0zbWuVPw/bEWdsUPJsCzjpbDi5F7ToBUvG2parcVfDwZ8h+wDUb2cTDXcYBJN/a7szQ3WZRqJLtSJISoVO19tWys/ug40zwZ8bfOJHJbhmbdFTZTd4sF3hYtOm8q+UEQehWuvy1ayaSMemNdl/6Cird9hVPAzQ9/T6jBnatVzn/nzlTu6esJR/De7EVZ2ikDZy0yZ4/HHbilqjRvFduRWAtuipsJV3rFksHNgB466CjPUBGwVOuxi63QGn9LFBGNjree962L0aTrvErs9aGuG0ohUu03EwtLoItsyBH+fAvhDrx3oTYeTeuE0qqBCfCxFspdQWPVX5PP+8nbQQTpB38CBUD/NbaozMu/l0Vtz/BH9o3Z/MpKpcuv57uh3ZRcqjD3Nuu6Y0q10FEeG34xbTqXktLu/QkN+NX8q3a/fw1epdXNKubGMNfU5r3qn107iiY4Rb8/Lt3w9ffgkrVtgWvgoc5ClVKiXN+oy33CPw1SPHg7yEZMjLgQbt4OdFNnVKjWZ2sH+nITD3edixFDxJcFmYyxcGCqdFKViZ9lfbB8CO5TDtj7B9iZ1cENhyVUFmjsZNJWixK4m26KnI8PtDr1VoDLRsCf37R3cpsdJatIi8Hj25/eqRzGnVjRFfv8JlGWtpuHltyGs5cDiXW95aSPr2A/zzujMYEOYkikBTVuzgXmdMYNQCPYBDh6CSJCGuEN/cVcUWajxaWfKeRUvWPjtgf9siqN+maDfntWPshIal78Cmb4IfI17XE+HxdZHgts8FbdFTldPBg3DllXbd1N/9LniZ3Fw7QaBt29jWrQQrG7Vi2F1vk3pSE6YMOYsJ3Zrz9N79jC5mce0aVRN5d3h3ho9dxP0Tl3M4x8cN3ZuHfU6f3/CvGetp3aAal7cvmqg4oipJkKfirDJ0hQLcuwLGXGwXtc9X73S45o341SnQvk0w/lo4uAMGjYW2Vx3fFxgwtRtoH9sWw5R77OQGiP+MTRe0XKngNNBT5VOtms2JV1xQkZQEf/xj7OoUhuxcH/d/sBxv40Z8OrwrNfbvDnvSQVpyAm/f2o07313CY5PTOZyTx/BeJU902HMwm+tf/x+b9mbx8pAz8XhO8C4RVTHMerZid4XmS//QCfLEjh3z5cDetfB6XztRoOfd0KBtbAPX/HOdfZ/NZwdwy2fQrFvJr23aBZr1tJMkvIl2lYayJP6NlEilLFEVTuimC6XCIQITJ8LNNwffn5sL06dDXl5s61WCZ75Yy6a9Wfz9uo7U+PvTNvHz5s1hvz4l0ctrN3XhsvYNGfX5GkbP3MDuA0cY9Op89hROYozNmffsl/acNasmlnl8n3KJaC2kXhqj6sOTNWDJGJuGY/Gb9vmoMqx4E20bZsCMP0G1Rjbv2e3fQJdhdlLDmbfA6knwck87W/Sz+44HrtGWvwrE+zdAlZowfEZ4QV6+/Fa04TPtT21FU1GgY/SK8847NrnsRRdF9zxu4PfDzJlw4YUFB+9+/rld7WHq1JBLe8XavI0ZDHljAbf0PIk/X9Xe5tWbPt2uEVvKgcd5Pj8Pf5zOx0u30bZRNdbszKR363r0Pb0+W/cd5qf9h5n+w26C3WWRyMXnFjEZi1ORuihLk1+tOGW9ptxs+HqEzZEWqEUvuObN+P99AmVstK12NZvBsK9t6ovCDu+Hf7Ry0ocUEo0xb5VhvKAL6Bi9yNAWvVCMgaefhjcqyPiPim7yZLj4Yvjqq4LbL7gAJk2qMMHygSO5PPjhCk6pl8ojlzm58Jo0gaFDyzS7LMHrYepKu9TPDzszMcC36/by+KereWPuj/yYkcW5p9bhlLpVSfDa46ckeriqU2PmPNwnQlelwhI4WzNe8lvRFr8ZmVa0slzTz4vg1V42yKt7GrYrNNnu2zIH1n9ZtrpEQ/ZBeP96m9R38ITgQR5A1dpw/2pod7XtBs1X73S4Z3Hk63X7d1A9YCJWQhXocB3clx75cylVThrohSICK1fC6NHxrknl0L8/vP++DewCpaTAwIHxT5LseHLKavZkHuWFQZ2okhSZlCNzHupD/zMak+gEckleD5e0a8DCxy5g+gPnM254D3q2rIvPb0hO8HA0T5cUi6lIB1dlcfSQDayqNwu+P6m6Xch+83fgc1qlgnXvHtpr03P8pXZ41xR4jNwj8PVIO6Eh5zDcOMkGel1ug9tnwpk3Q2o9u07nzKeCrwhQHqXtrvb7YNLtsH8zDHoHap1UfPlqDSGlpn1dfuC6d63NUbcrggHYhhk2R97BHYDYVrx4j69Tqhg6GaM4SUl2ooEqWWKinXkb6OuvYeNGuP12uz/OpqXvZPKy7dx3QSvOaFYzYsetXz2FaikJ5DmBXI7PT720ZOpXPx7IZRw6ypDuJxVYUkzFwM6VcHo/WP1JwHJQAqdfDv1eiN5587tUL3gSVk+262YePQiNOtnu0a3zbBqLvKNw8vl2JYRl420wWKW2rV/mHjv+64MboUYTm+Ps2IxTgeQakHOo4KLqNZrDjCfhtMvsYP/8Fr/PH7BBz76NcNZQuOgpe85TA76Y9f+3Xdbq8wdgzj/gwM/Q/z92uatImPU3ez3hTvr49q+2dfHyf0CLc8M7R+GZo7tW2r/Za32g98Nwzv0Fl/wqjZzDdpzgwtegXhuo2xrqttJZqqrC0zF6wWRl2ZQhI0dC377ROYdbTZxo11kdPRp++1sb7G3eHPekm3sOZnPxi7NpXrsqH991NoneyDZmx3ptWLeJyFic/ODq6jfsouMLXoOfvreLkNdoZhPY5s/WTEqDu+ZBrRaRqH5Bfj9M+A1s/No+9yRCuwHQ7bc2+PrgRkhrUDBAGDzeBhIbZ8CHtwRfo1Q8cOGf7TEanWGXvsrPe5Z31E4C8CbB1u8LBn+BvInweEbx9TfGBnrfjLJB6W/etctrlTQWMHC8YFp9OLDN5pP7eHjw+niT4fEQ49lWfQwf3WYnWlz5r/J9fhzeD9MetMdsfCYMfAVSapRubOOO5bZ1MWM99PgdXPAnSNQW+WjTMXoROq8GekGsXWtbp0aPhvPPj8453Oqpp+x4vblzoUoVyMiAevWidro9B7O5x0k+HKordPeBI1z84hyO5OQx7b7zOLW+5pcLSwwnL0TkA/2T38HyCTawy82CmifZ5aY6D4FP7zkeXH33d1j3OaQ1hFumQJ2WEbkG/H7bderPLbqvNIP0M3fBl4/C2qk2KA22vibA+0OCB4xHfoX0j2Dei7ZVDuwi7G37w6XPhv9vueID+PRu+/dp2BFWfVR08ogxkP0rZGXYbuH1X0HN5jbwPLTLlvEmQ3KaXTc1cK1Vbwq06WeX42rZxwahmbtgwiDYsw4ad7KpSiLVorh6sp0Ek5NlA+Xti4ufDJO5Cz681S5c//1oSK0PA/5r66piQgO9CJ1XA71iGBP3lqhKJyfHdtPG6O82cnI64xf+xJBuzRk1sEPQMoNenc/CH/fT5aRafHTX2TGplytEamZoGMr1gV6WGZA7V8K4Aba17ZYpUK912c4NdkzY6skw+x+wd41tLfTlOEFaQBLc0gTLkVilIP8YnkQbfJblGE/Vtd25RYgdE5eVETywBfAkwLDp0KA9fPFwwetp3c+2+q2eBEd+gap1of018OtW212bmAr3LbdlIump+nY8XZHL8UKv/4OqdSC1rv258HX7hQBsguN+z9tJHypmNNCL0Hk10Cvk6FG7dquuDVo+l18OPXvC449H5fCtR37B0byi3VteEQZ0bsKBI7nMXLs76HhyTWtSglinjsjcxa9vXE3N4ZPL1nI461mY9TTHWorCDa72rIGx/W036c2fQsPwEmYX6CLeMhdm/90uCl/vdDjvj3bb0rHlC9JCtdbF+hiZu2DK72HDdMAAAlVq2bGGNRrbyRsJybDxG9i5PHRwG6oueTmwcbrtzg7WXR3p91zmLvhqBKyZYusqHhtUehNsS2jQREhRqosqkQZ6kaGTMQobOxaeeAKWLbM59FTp5eTYrtsoplSZ81Af7p6wlEVbfimwPTnRw/8276N6lUQ6N6vJjgNH2JuZg89vSEn0cEm7hozo1yZq9XKF+1bCJ3fDphn2uTfZdvuVZWmmcLp/v3uOGgd+KP3KDH6f7eJc+KrtLjzws+0ODHcGZP02cOs0GHsljL0CbvrEtlKVVN9Zz9hJBf/tDkczoX47uG4stOlv10hePbn8S0lFYpWCSByjWkOo3tS20HuTbXDUbmDR42XusV2hCSH+/qHqkpBkJ8s8sAamPQTrp9kWxGgtB1atoa2bP8+paw50HGTr5PfZYG/vWpj9nB3rWDhwVaoS0kCvsNat4dproaGuXFBmSUmwYwekRu+Ly9yNGceCvCSvh1y/n+u7NuPpqzsWKDdicjoTFv6kaU1KI/cIbJ3rPBH7H7c/r2ytbcfyvD1jB7BnZUDWXvv46NZjCW4FbIqQxW+G13KSkwUfDYP1X0DPe+CXLXDqRaUPruq2coK9/vZx8nkFlwPz+216j10ri04qOJppf+7faCdb5HPbUlLhrIFa3nVSqzW03aV+X+hgMVJC1dXjhdQ6kHoOpJ8CP86Ofl2UigHtulWVzrv/28rIT1ZROzWRi9s25OaeLULOdNXZsKWUtQ/evMiOlWp7FZxxPUy82Xar/X6pTfMRjlDdvyWp2QIGvAwtihlLmbnLzmrdtRIuew663V768xT2cx3KkAAAGM9JREFUVD3belMc8drxd/kpTco6/k4FF4muZjfW5QSmXbcROq8GegGWL4emTaFu3cgeV0XM67M389dpa7jg9Pq8NORMUhJ1LGXE5GTZVq3dq+yYteY97PZd6TDmMrsE1a1f2DU9S3JwJ4y/1h4L7MD8+u2g8412FmdqPfuY9Qwsewc/Xjwm1wZTxm9nmfZ6ABp3tq/P7wLu/ZhdPP7wPrj2LWh9aWSuPXOXzR+3dhrHxmml1rP56Jp3h4Yd7Bi8Lx4p/yQJpVRYNNCLDO26DTR0KKSl2fFlqkIxxvCvmRt4ccYG+nVsxIu/6RTxXHiVRjTSnvjybN6yHUth0LjjQR7YIGfwu/DutXbQ/I0f2+7VUPKOwsw/Hw/yEpLtuKumXaD7HQXLHs6As25lCR3oSrodZ9ewo53xuGYKtOwL5z5gZ2dunQ/jBtouvlu/sOk3IqVaQ0htYMeieRJtl3Kb/kWDuPJ2USqlVIxpi16g5cttsuRzzonscVW5GGN4etoaXp/zI9ee1ZRnr+mI13MCp72JdNoTY+yyV0vfgX7/hK7Dg5dbOdEmjW1/LVz9up14UNihvfDBEPh5gV1eq0WvsLq/inxzzz4Ii8fYlQiCicYMSO2uU6pC0Ra9CJ1XAz0VDeEkMg7rGBOW0rRWVSYt284tPU/iT1e2w3OiBnnRSnsy6xm7PFWvB+GCEtLhzH3BLrF19r1w8VMF9+1aBe8NthMuBr5sZ2eGW4VQH+j7t8BHQ+3KBJjQyYOVUq6jgV5knKB9X4UYY1d0WLcu3jVxjdEzN7Boy35Gz9gQdP+eg9kMenU+e4pZ8/XFGetZuOUXJi3bzp3nt+TJ/idwkHc0E864AWd+agCBJl2cFqi9xzeXtIB8/v55o22Qd8YN0HdkyfU45w/Q9Xa7UsCCV48fZ9kEePNi2+V52xelCvKKVbsFNOpsu1Tz02HoDEillAqbtuiBXfKsQwd48024+ebIHPMEFSqRcZLXw/8eu4DqKQkkeD3HVrS4oVtz7ul7Klv3HeanfYfZuj+Ll2dtwq+Jji1j7BqdX4+0QVXdVpCxweYfy8uxSX5zsmwKEPFA8552bNmOpZD+Yeju3akP2K5RDJx6IVz/vl2CKhx+n52Ju/ZzuxzUpm/tcRp3hsHvQfXS558s9pu7dqkqdULSFr0Inde1gZ7PB4sWQY8eocsE2rfPrs1atWpkKniCmr8pg9+NX8ovh0Msi1QCr0doWD2FnDwf+w/nFkl07PoceIETLY78Yhdj3zLHrkTQ75+267Rw0PObd2H3ajt54bvnCJndv25ruyh7sP2l7f6NcDey2z7QlVLl57bPBZ11G2kvvQR/+AMsWQKdO5dcvk6d6NfJxbJzfbz07UZe+W4Tgu1gTPR6yPX56d26Hv07NebA4Vy2/5rNzDW72bovC5+xgV27xtUZfu7JnNGsJo1rViHR66l4iY4jMdM1zFUi+Gk+vHu1zdCflGbX2DxrqE3oGioZb8P29tHlNphyL2z6xq5B6kmwq0Y07GjTlqQ1sEt/Hc6gyJi30rhvJUz7I6ydao+rqwcopVSF5N5Ab/hwmyqlUwkpGGbMgP/+1waGuuRZmczftI/HJqfzY0YWV3duwi+Hc2hSq2qBJMUDOzc9Vv5wTh4/7ssiOcFDjs9PxyY16N+pYCLejENHGdL9pALHiKtZf7MBWHHLdJUUyB1bJcI5hjGQ/Sv8+jO83rfg4vD5qUlyj0DXYeHXs1pDqN7ESejrjGk7pU/BOucvdp+/pFVZxrxVa2gXogddPUAppSow9wZ6VavCbbfZ33/6CW6/HV59FVq0KFhuzx47CaN27ZhXsbLKn1H79MD2vDZ7MxMXb6N57aqMG9aNXq3qFSg7akDRheLDCeJevamLEzj9hlHFtYCF00pWUplg+w/usClCPhpWcNmr/GW6xGOX3qrZHGq1sD/n/8fmevt6JJz9exvEHfm1wFJfBY5RnPKsL1tSrrdI5YLTnHJKKVXhuXeMXqDZs+Gmm+Crr+D004vuN8bO6ouTSKQiiaWRk9MZv+AnkhI85PkNd5x3Cvf2bUWVpFKsUhFOgBZOvriSyvjyYOp9sGw8dLoBLnzSTiYwfufhg29GQfpH0KybbQ37eSEc3GZf702G5Go2aPPn2ZUbUutCYhUbDJa0bFZQAtUbQ8sLoF5rqNHUrjqx8HVY+cEJueqC28biKKXKz22fCyf8ZAwRuRT4F+AF3jDGPFNc+ZICvYwdW9nz1g3UH/YedRs2h5wcSEqyO+fPhx49yNi8jj3vDztepqRjhHOeMpR5ZuK39Fn1CN+2f5ZHBvWO2nnCKRNsf06en3W7Mhn433nk+Q31+IX/JP2be3LuZS81g8+GLSmQCxag5WTZ4OnlnnYlhcLEa8eqGb9N7hvY0na8ENQ6yR4ra2+Q/WEQD1zytA38GnSALx4OvuyV329bsXYsg7kvwo4lNhj0JtqUJ93vhNqn2CXDZj0LKyYUH8SdwLNL3faBrpQqP7d9LoQT6JUUC4mIOPsvBw4DQ40xS4s7ZoXouhURL/AScBGwDVgkIlOMMT+U9Zir3x9Br5zVfP/+CFoPe4PUZC9VjEG++w769IFx4/A++yhtsnexsPrj1P392CLH2PTxE3TNWc2iD4PvL2uZ7Fwfuw5kc9EL35HrMzyVMIau3nWsX/lvWizNItErzH6oD/WrpRxbAaI051k48XFS7xyDzxh8/uMPvzFsmDiSs52/y8lDXyMw0DcGtjr7p7/zCN+1epT0bQdYtyuTHJ9NmZLgEe71TKarrOP+pMksaDuCEf3aFKyIMTYJ79b58NUI6HkXZB+w3ZiTbi99NybYZamSUuGHT20gVqW2XVw+74hTQGxLW4MOkFrHtsIhdkxcxnpnYkKiXc6r7QAbfB3NhNWTYddKG3yFSsYbqovS47GpRKo3gg3TYfui4+Pi6reFdgOOHyP715K7OUNNtFBKKeV6YcZClwGtnEd34GXnZ+jjVoQWPRHpCTxpjLnEef4ogDHmb6FeE6pFL/tPdUmRoq1BecbDe/6+JIgwaNk0vJ0SYHku+IGuScfKTK9yGRcd+YIEKZoLLs94mFfjCjweDz1/mRKyzJK6/QE4KyN4GZ8RZvrP5ALPUrxS9O/vM8LX/i7kkcDlnv+FKONhVkof/D4ffXO/C3mc6f4uAFzkWRy0jN8IC/xt6O5Zgyfofsio2pIqiR7SDm5EQqXuSK5uW+GOBV7hEqjWyOZjq3Oq7Tqt3hiWvG3XNy2uBezYpIJylAnnGOE4gVvjIsFt39yVUuXnts+Fklr0womFRORVYJYx5j3n+TqgtzFmZ8jjVpBA71rgUmPMcOf5TUB3Y8w9hcrdAdwBkJCQcNb06dOLHOvIwQyqpY/h7Jx5eMQ2LuVIArmSAiJ2OJ7xk8RREk0ekl+GBLIlGYMXwU+KySaJgvuPSArGWZlAjI8q5BQpc5hkDB4MguCnKtkkO2X8Bg5LClnemuBNxuuBpOx9pJosvGLwGSFLUslJroXPD/jz8PiOkmYOkULOsfNkk0iWpOKXBIx4wBhSTSZVTTYe5zxZUoUsby2bXgPA+EjN+4VUcwSPGPxGOCipZCY1wO9Jwvh9VMvZTU1z8FhdfvXUIq96czyJKYAg/jz8v/xIHbMfL358eNgt9ZD67fAlpOL3ePH4c6n5SzpVj2zHY3z4JYED1VuzrelVZFdpSG5iGi1+nECjXTPxexLw+PPY0egSNrS+q8C/Y7tVfyMnqRY7Gl9C4x1fkZTzC6vbPxrxMuEcQ0XfoUOHSEtLi3c1lFIViNs+F/r06ZMDpAdses0Y81r+k3BiIRGZCjxjjJnrPJ8JPGyMWRzqvBWi65ai6zpBkKyuzh/kNbAteqEi/QVbp0CGkG0SSCKP5XWuoHuh7s4F/76ZrhlTOJpfpm7BMiXtL2uZ1XUuLVDmu3/eSK/Mqfi9yYgvh6XV+9D7gXeLPcbKuv1Cnif/mn+oc0kxZRJJIo91dS4Mes35+zfWPq/IMY63gKXg9eXQ+KwrQreiJaTg8eVQq1UPal3x8PH970+CprfhdVq/mhzaTZPC/5bOc5t05Va7CSJfJpxjqKhz2zd3pVT5ufBzIc8Y06WY/eHEQmHFS4EqSqC3DWgW8LwpsKOsB0s8so+FdQdQr/ed7J31CklHig7KL6lMJI4RTpnzmxhIu+1Yl1/vIGO3YlWXcI4RVkqNksroWDSllFKqsHBioVLHSxWl6zYBWA9cAGwHFgE3GGNWh3pNRNe6VUrFlQu/uSulysltnwthjNErMRYSkX7APdhZt92B0caYbsWdt0K06Blj8kTkHuAr7JTiMcUFeUoppZRSbhIqFhKRO539rwDTsEHeRmx6lVtLOm6FCPQAjDHTsBeglFJKKXXCCRYLOQFe/u8GuLs0x/REpmpKKaWUUqqi0UBPKaWUUsqlNNBTSimllHIpDfSUUkoppVxKAz2llFJKKZfSQE8ppZRSyqU00FNKKaWUcikN9JRSSimlXEoDPaWUUkopl6oQa92WhYj4gSMlFPMCvnKWicQxKtJ5KlJd9JpPjLqEc4wEIK+C1KUi/V0q03kqUl30mt1Rl5I+FyrbNVcxxsS+gc0YUykfwOIwyrxW3jKROEZFOk9Fqote84lRlzCP4ar7uSLVRa/5xKiLS6+52M+FSnjNJX7ORePh9q7bzyJQJhLHqEjnqUh10Ws+MeoSzjHCUZmuuSLVRa/5xKiLG685EseoSNccF5W563axMaZLvOuhlCo/vZ+VUoW57XMhXtdTmVv0Xot3BZRSEaP3s1KqMLd9LsTleipti55SSimllCpeZW7Rq7BE5FIRWSciG0XkEWfbGSIyX0TSReQzEake73pGioiMEZE9IrKq0PbfO3+H1SLyXLzqFw0i0kxEvhWRNc713edsf0pEVorIchH5WkQax7uukSIiKSKyUERWONf8Z2d7bRGZLiIbnJ+14l3XSAp2Pzvb3fz+LnJPu/y9Hep+du17u5hr/sD5N14uIltEZHm86xpJoe5nZ9+DImJEpG686hcV8ZgB4uYHdor1JuAUIAlYAbQFFgHnO2VuA56Kd10jeM3nAWcCqwK29QFmAMnO8/rxrmeEr7kRcKbzezVgvfPvXD2gzL3AK/GuawSvWYA05/dEYAHQA3gOeMTZ/gjwbLzrGsFrDnU/u/39HeyedvN7O9T97Ob3dtBrLlTmn8AT8a5rBK856P3s7GsGfAVsBerGu66RfGiLXuR1AzYaYzYbY3KA94GrgNbAbKfMdOCaONUv4owxs4H9hTbfBTxjjDnqlNkT84pFkTFmpzFmqfN7JrAGaGKMORhQLBVwzdgIYx1yniY6D4N9f491to8FBsShetES6n52+/u7yD3t8vd20PsZF7+3i7lmAEREgEHAe/GpYVSEup8BXgAewkXv63wa6EVeE+DngOfbnG2rgP7Otuuw3x7c7DSgl4gsEJHvRKRrvCsULSLSAuiMbeFCRP4qIj8DQ4An4lezyBMRr9OVsweYboxZADQwxuwE+58HUD+edYywUPfzCfP+DuTm93a+Qvezm9/bxxT+DHP0AnYbYzbEo05REvR+FpH+wHZjzIr4VCu6NNCLPAmyzWC7a+8WkSXYZvKcmNYq9hKAWtiuvT8CE51viK4iImnAx8Af8ls8jDEjjDHNgPHAPfGsX6QZY3zGmE5AU6CbiLSPd52iLNT9fEK8vwtz83sbgt/PblfMNV+Pu1rzIPj9nAyMwKVfXEADvWjYRsHWuqbADmPMWmPMxcaYs7A3z6a41C52tgGTnO6+hYAfcNUAVxFJxH5AjjfGTApSZAIu6qIPZIz5FZgFXArsFpFGAM5PN3VjBr2fOQHe3yVw3Xs7xP3s5vd2yM8wEUkArgY+iFfdoiTY/fwTcDKwQkS2ONuWikjD2FcvOjTQi7xFQCsROVlEkoDBwBQRqQ8gIh5gJPBKHOsYC58AfQFE5DTswNeMuNYogpzWmzeBNcaY5wO2twoo1h9YG+u6RYuI1BORms7vVYALsdc3BbjFKXYL8Gl8ahgVQe9nXP7+Dsbl7+2g9zMufm8Xc83g3NvGmG2xr1lUBbufJxlj6htjWhhjWmCDwTONMbviWdFISoh3BdzGGJMnIvdgZ+94gTHGmNUicp+I3O0UmwS8FbdKRpiIvAf0BuqKyDbgT8AYYIyTniEHuMU4U5tc4hzgJiA9IP3AY8AwEWmNbeHZCtwZp/pFQyNgrIh4sV8SJxpjporIfGzX5TDst+Pr4lnJSCrmft6Ai9/fIe7py1383g51Pz+DS9/bhLhmY8w0bADktm7bkPdznKsVdZowWSmllFLKpbTrVimllFLKpTTQU0oppZRyKQ30lFJKKaVcSgM9pZRSSimX0kBPKaWUUsqlNNBTSimllHIpDfSUUkoppVxKAz2llFJKKZfSQE8ppZRSyqU00FNKKaWUcikN9JRSSimlXEoDPaWUUkopl9JATymllFLKpTTQU0oppZRyKQ30lFJKKaVcSgM9pZRSSimX0kBPKRUzIjJQRIyInB7vuiil4sP5DBgX8DxBRPaKyNR41sutNNBTSsXS9cBcYHBpXiQi3uhURykVB1lAexGp4jy/CNgex/q4mgZ6SqmYEJE04BxgGE6gJyK9RWS2iEwWkR9E5BUR8Tj7DonIX0RkAdAzfjVXSkXBF0A/5/frgffyd4hINxH5XkSWOT9bO9vniEingHLzRKRjTGtdCWmgp5SKlQHAl8aY9cB+ETnT2d4N+D+gA9ASuNrZngqsMsZ0N8bMjXltlVLR9D4wWERSgI7AgoB9a4HzjDGdgSeAp53tbwBDAUTkNCDZGLMyZjWupDTQU0rFyvXYD3ecn9c7vy80xmw2xviw3+rPdbb7gI9jW0WlVCw4AVoL7OfAtEK7awAfisgq4AWgnbP9Q+AKEUkEbgPejkllK7mEeFdAKeV+IlIH6Isdl2MAL2CwH/CmUPH859lO8KeUcqcpwD+A3kCdgO1PAd8aYwaKSAtgFoAx5rCITAeuAgYBXWJY10pLW/SUUrFwLfCOMeYkY0wLY0wz4Eds6103ETnZGZv3G+xkDaWU+40B/mKMSS+0vQbHJ2cMLbTvDWA0sMgYsz+61XMHDfSUUrFwPTC50LaPgRuA+cAzwCps8Fe4nFLKhYwx24wx/wqy6zngbyIyD9v6H/iaJcBB4K0YVNEVxJjCvSZKKRUbItIbeNAYc0W866KUqvhEpDG2K/d0Y4w/ztWpFLRFTymllFIVnojcjJ2dO0KDvPBpi55SSimllEtpi55SKuJEpJmIfCsia0RktYjc52yvLSLTRWSD87OWs/0iEVkiIunOz74BxzrL2b5RREaLiMTrupRSqrLRQE8pFQ15wP8ZY9oAPYC7RaQt8Agw0xjTCpjpPAfIAK40xnQAbgHGBRzrZeAOoJXzuDQ2l6CUUpWfBnpKqYgzxuw0xix1fs8E1gBNsPmvxjrFxmJXy8AYs8wYs8PZvhpIEZFkEWkEVDfGzDd2nMk7+a9RSilVMg30lFJR5SQ87YwdRN3AGLMTbDAI1A/ykmuAZcaYo9jgcFvAvm3ONqWUUmHQlTGUUlEjImnYfHl/MMYcLGl4nYi0A54FLs7fFKSYziBTSqkwaYueUioqnPUoPwbGG2MmOZt3O92xOD/3BJRvik2WfLMxZpOzeRvQNOCwTYEdKKWUCosGekqpiHNmxr4JrDHGPB+wawp2sgXOz0+d8jWBz4FHjTHz8gs73buZItLDOebN+a9RSilVMs2jp5SKOBE5F5gDpAP5iU0fw47Tmwg0B34CrjPG7BeRkcCjwIaAw1xsjNkjIl2At4EqwBfA741+cCmlVFg00FNKKaWUcintulVKKaWUcikN9JRSSimlXEoDPaWUUkopl9JATymllFLKpTTQU0oppZRyKQ30lFInHBF5UkQeLGb/ABFpG8s6KaVUNGigp5RSRQ0ANNBTSlV6mkdPKXVCEJER2JU1fgb2AkuAA8AdQBKwEbgJ6ARMdfYdAK5xDvESUA84DNxujFkby/orpVRZaKCnlHI9ETkLu7pGdyABWAq8ArxljNnnlBkF7DbG/FtE3gamGmM+cvbNBO40xmwQke7A34wxfWN/JUopVToJ8a6AUkrFQC9gsjHmMICITHG2t3cCvJpAGvBV4ReKSBpwNvChXW4XgOSo11gppSJAAz2l1IkiWPfF28AAY8wKERkK9A5SxgP8aozpFL2qKaVUdOhkDKXUiWA2MFBEqohINeBKZ3s1YKeIJAJDAspnOvswxhwEfhSR6wDEOiN2VVdKqbLTMXpKqRNCwGSMrcA24AcgC3jI2ZYOVDPGDBWRc4DXgaPAtYAfeBloBCQC7xtj/hLzi1BKqVLSQE8ppZRSyqW061YppZRSyqU00FNKKaWUcikN9JRSSimlXEoDPaWUUkopl9JATymllFLKpTTQU0oppZRyKQ30lFJKKaVcSgM9pZRSSimX+n9dEj0+eM455gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig, ax = plt.subplots()\n",
    "ax = frm.plot(y=['totalTestResultsIncrease', 'positiveIncrease'], grid=True, style='-*')\n",
    "ax.legend(loc='upper left')\n",
    "ax2 = ax.twinx()\n",
    "frm.plot(y='dailyPositiveRate', ax=ax2, color='red', style=':', figsize=(10, 6))\n",
    "ax2.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "frm.to_csv('../outputs/frm_2020-04-29.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24f789bf128>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frm.plot(y=['hospitalizedCurrently', 'inIcuCurrently'], grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24f7cc07780>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frm.plot(y=['total'], grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CDC Tests Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdc_link = ''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Kaggle Data contains some medical info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jian\\Anaconda3\\envs\\research\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3063: DtypeWarning: Columns (1,2,10,11,13,14,15,16,17,18,19,20,22,23,24,25,26,27,31,32) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('https://raw.githubusercontent.com/beoutbreakprepared/nCoV2019/master/latest_data/latestdata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(261558, 34)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "      <th>country</th>\n",
       "      <th>wuhan(0)_not_wuhan(1)</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>geo_resolution</th>\n",
       "      <th>...</th>\n",
       "      <th>date_death_or_discharge</th>\n",
       "      <th>notes_for_discussion</th>\n",
       "      <th>location</th>\n",
       "      <th>admin3</th>\n",
       "      <th>admin2</th>\n",
       "      <th>admin1</th>\n",
       "      <th>country_new</th>\n",
       "      <th>admin_id</th>\n",
       "      <th>data_moderator_initials</th>\n",
       "      <th>travel_history_binary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000-1-</td>\n",
       "      <td>30-39</td>\n",
       "      <td>female</td>\n",
       "      <td>Snohomish County</td>\n",
       "      <td>Washington</td>\n",
       "      <td>United States</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.048180</td>\n",
       "      <td>-121.696000</td>\n",
       "      <td>admin2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snohomish County</td>\n",
       "      <td>Washington</td>\n",
       "      <td>United States</td>\n",
       "      <td>2988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000-1-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Khuzestan</td>\n",
       "      <td>Iran</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.496225</td>\n",
       "      <td>48.967279</td>\n",
       "      <td>admin1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Khuzestan</td>\n",
       "      <td>Iran</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000-1-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000-1-</td>\n",
       "      <td>50-59</td>\n",
       "      <td>male</td>\n",
       "      <td>Snohomish County</td>\n",
       "      <td>Washington</td>\n",
       "      <td>United States</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.048180</td>\n",
       "      <td>-121.696000</td>\n",
       "      <td>admin2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snohomish County</td>\n",
       "      <td>Washington</td>\n",
       "      <td>United States</td>\n",
       "      <td>2988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000-1-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pays de la Loire</td>\n",
       "      <td>France</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47.486460</td>\n",
       "      <td>-0.811280</td>\n",
       "      <td>admin1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pays de la Loire</td>\n",
       "      <td>France</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID    age     sex              city          province        country  \\\n",
       "0  000-1-  30-39  female  Snohomish County        Washington  United States   \n",
       "1  000-1-    NaN     NaN               NaN         Khuzestan           Iran   \n",
       "2  000-1-    NaN     NaN               NaN               NaN            NaN   \n",
       "3  000-1-  50-59    male  Snohomish County        Washington  United States   \n",
       "4  000-1-    NaN     NaN               NaN  Pays de la Loire         France   \n",
       "\n",
       "   wuhan(0)_not_wuhan(1)   latitude   longitude geo_resolution  ...  \\\n",
       "0                    1.0  48.048180 -121.696000         admin2  ...   \n",
       "1                    1.0  31.496225   48.967279         admin1  ...   \n",
       "2                    NaN        NaN         NaN            NaN  ...   \n",
       "3                    1.0  48.048180 -121.696000         admin2  ...   \n",
       "4                    1.0  47.486460   -0.811280         admin1  ...   \n",
       "\n",
       "  date_death_or_discharge notes_for_discussion location admin3  \\\n",
       "0                     NaN                  NaN      NaN    NaN   \n",
       "1                     NaN                  NaN      NaN    NaN   \n",
       "2                     NaN                  NaN      NaN    NaN   \n",
       "3                     NaN                  NaN      NaN    NaN   \n",
       "4                     NaN                  NaN      NaN    NaN   \n",
       "\n",
       "             admin2            admin1    country_new admin_id  \\\n",
       "0  Snohomish County        Washington  United States     2988   \n",
       "1               NaN         Khuzestan           Iran       15   \n",
       "2               NaN               NaN            NaN      NaN   \n",
       "3  Snohomish County        Washington  United States     2988   \n",
       "4               NaN  Pays de la Loire         France       12   \n",
       "\n",
       "  data_moderator_initials travel_history_binary  \n",
       "0                     NaN                   NaN  \n",
       "1                     NaN                   NaN  \n",
       "2                     NaN                   NaN  \n",
       "3                     NaN                   NaN  \n",
       "4                     NaN                   NaN  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. NYTimes Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyt_link_county = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv'\n",
    "nyt_link_state = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyt_county = pd.read_csv(nyt_link_county)\n",
    "nyt_state = pd.read_csv(nyt_link_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyt_county.to_csv('nyt_county.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>fips</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23962</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>Sheridan</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56033.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23963</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>Sublette</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56035.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23964</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>Sweetwater</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56037.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23965</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56039.0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23966</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>Washakie</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56043.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date      county    state     fips  cases  deaths\n",
       "23962  2020-03-31    Sheridan  Wyoming  56033.0     10       0\n",
       "23963  2020-03-31    Sublette  Wyoming  56035.0      1       0\n",
       "23964  2020-03-31  Sweetwater  Wyoming  56037.0      2       0\n",
       "23965  2020-03-31       Teton  Wyoming  56039.0     23       0\n",
       "23966  2020-03-31    Washakie  Wyoming  56043.0      1       0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyt_county.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.pivot_table(nyt_county, index=['state'], columns=['date'], values=['cases'], aggfunc='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       date      \n",
       "cases  2020-01-21       NaN\n",
       "       2020-01-22       NaN\n",
       "       2020-01-23       NaN\n",
       "       2020-01-24       1.0\n",
       "       2020-01-25       1.0\n",
       "                      ...  \n",
       "       2020-03-27    3087.0\n",
       "       2020-03-28    3602.0\n",
       "       2020-03-29    4673.0\n",
       "       2020-03-30    5125.0\n",
       "       2020-03-31    6046.0\n",
       "Name: Illinois, Length: 71, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.loc['Illinois']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
       "      <th>fips</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>17</td>\n",
       "      <td>3029</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1403</th>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>17</td>\n",
       "      <td>3547</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>17</td>\n",
       "      <td>4613</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1513</th>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>17</td>\n",
       "      <td>5070</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1568</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>17</td>\n",
       "      <td>5994</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date     state  fips  cases  deaths\n",
       "3     2020-01-24  Illinois    17      1       0\n",
       "6     2020-01-25  Illinois    17      1       0\n",
       "10    2020-01-26  Illinois    17      1       0\n",
       "14    2020-01-27  Illinois    17      1       0\n",
       "18    2020-01-28  Illinois    17      1       0\n",
       "...          ...       ...   ...    ...     ...\n",
       "1349  2020-03-27  Illinois    17   3029      37\n",
       "1403  2020-03-28  Illinois    17   3547      50\n",
       "1458  2020-03-29  Illinois    17   4613      70\n",
       "1513  2020-03-30  Illinois    17   5070      84\n",
       "1568  2020-03-31  Illinois    17   5994     107\n",
       "\n",
       "[68 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyt_state[nyt_state['state'] == 'Illinois']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. USA Facts Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "usafacts_confirm_link = 'https://usafactsstatic.blob.core.windows.net/public/data/covid-19/covid_confirmed_usafacts.csv'\n",
    "usafacts_death_link = 'https://usafactsstatic.blob.core.windows.net/public/data/covid-19/covid_deaths_usafacts.csv'\n"
   ]
  }
 ],
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